The rapid spread of information via social media has made text-based fake news detection critically important due to its societal impact.This paper presents a novel detection method called Dynamic Representation and Ensemble Selection (DRES) for identifying fake news based solely on text.DRES leverages instance hardness measures to estimate the classification difficulty for each news article across multiple textual feature representations.By dynamically selecting the textual representation and the most competent ensemble of classifiers for each instance, DRES significantly enhances prediction accuracy.Extensive experiments show that DRES achieves notable improvements over state-of-the-art methods, confirming the effectiveness of representation selection based on instance hardness and dynamic ensemble selection in boosting performance.Codes and data are available at: https://github.com/ FFarhangian/FakeNewsDetection_DRES.

Solutions to the Algorithm Selection Problem (ASP) in machine learning face the challenge of high computational costs associated with evaluating various algorithms' performances on a given dataset. To mitigate this cost, the meta-learning field can leverage previously executed experiments shared in online repositories such as OpenML. OpenML provides an extensive collection of machine learning experiments. However, an analysis of OpenML’s records reveals limitations. It lacks diversity in pipelines, specifically when exploring data preprocessing steps/blocks, such as scaling or imputation, resulting in limited representation. Its experiments are often focused on a few popular techniques within each pipeline block, leading to an imbalanced sample. To overcome the observed limitations of OpenML, we propose PIPES, a collection of experiments involving multiple pipelines designed to represent all combinations of the selected sets of techniques, aiming at diversity and completeness. PIPES stores the results of experiments performed applying 9,408 pipelines to 300 datasets. It includes detailed information on the pipeline blocks, training and testing times, predictions, performances, and the eventual error messages. This comprehensive collection of results allows researchers to perform analyses across diverse and representative pipelines and datasets. PIPES also offers potential for expansion, as additional data and experiments can be incorporated to support the meta-learning community further. The data, code, supplementary material, and all experiments can be found at https://github.com/cynthiamaia/PIPES.git.

Psychological biases, such as confirmation bias, make individuals particularly vulnerable to believing and spreading fake news on social media, leading to significant consequences in domains such as public health and politics. Machine learning–based fact-checking systems have been widely studied to mitigate this problem. Among them, ensemble methods are particularly effective in combining multiple classifiers to improve robustness. However, their performance heavily depends on the diversity of the constituent classifiers—selecting genuinely diverse models remains a key challenge, especially when models tend to learn redundant patterns. In this work, we propose a novel automatic classifier selection approach that prioritizes diversity, also extended by performance. The method first computes pairwise diversity between classifiers and applies hierarchical clustering to organize them into groups at different levels of granularity. A HierarchySelect then explores these hierarchical levels to select one pool of classifiers per level, each representing a distinct intra-pool diversity. The most diverse pool is identified and selected for ensemble construction from these. The selection process incorporates an evaluation metric reflecting each classifier’s performance to ensure the ensemble also generalises well. We conduct experiments with 40 heterogeneous classifiers across six datasets from different application domains and with varying numbers of classes. Our method is compared against the Elbow heuristic and state-of-the-art baselines. Results show that our approach achieves the highest accuracy on two of six datasets. The implementation details are available on the project’s repository: https://github.com/SaraBCoutinho/HSFN.

Abstract Hate speech is a growing problem on social media due to the larger volume of content being shared. Recent works demonstrated the usefulness of distinct machine learning algorithms combined with natural language processing techniques to detect hateful content. However, when not constructed with the necessary care, learning models can magnify discriminatory behaviour and lead the model to incorrectly associate comments with specific identity terms (e.g., woman, black, and gay) with a particular class, such as hate speech. Moreover, some specific characteristics should be considered in the test set when evaluating the presence of bias, considering that the test set can follow the same biased distribution of the training set and compromise the results obtained by the bias metrics. This work argues that considering the potential bias in hate speech detection is needed and focuses on developing an intelligent system to address these limitations. Firstly, we proposed a comprehensive, unbiased dataset to unintended gender bias evaluation. Secondly, we propose a framework to help analyse bias from feature extraction techniques. Then, we evaluate several state-of-the-art feature extraction techniques, specifically focusing on the bias towards identity terms. We consider six feature extraction techniques, including TF, TF-IDF, FastText, GloVe, BERT, and RoBERTa, and six classifiers, LR, DT, SVM, XGB, MLP, and RF. The experimental study across hate speech datasets and a range of classification and unintended bias metrics demonstrates that the choice of the feature extraction technique can impact the bias on predictions, and its effectiveness can depend on the dataset analysed. For instance, combining TF and TF-IDF with DT and MLP resulted in higher bias, while BERT and RoBERTa showed lower bias with the same classifier for the HE and WH datasets. The proposed dataset and source code will be publicly available when the paper is published.

Dataset scaling, a.k.a. normalization, is an essential preprocessing step in a machine learning (ML) pipeline. It aims to adjust the scale of attributes in a way that they all vary within the same range. This transformation is known to improve the performance of classification models. Still, there are several scaling techniques (STs) to choose from, and no ST is guaranteed to be the best for a dataset regardless of the classifier chosen. It is thus a problem- and classifier-dependent decision. Furthermore, there can be a huge difference in performance when selecting the wrong technique; hence, it should not be neglected. That said, the trial-and-error process of finding the most suitable technique for a particular dataset can be unfeasible. As an alternative, we propose the Meta-scaler, which uses meta-learning (MtL) to build meta-models to automatically select the best ST for a given dataset and classification algorithm. The meta-models learn to represent the relationship between meta-features extracted from the datasets and the performance of specific classification algorithms on these datasets when scaled with different techniques. Our experiments using 12 base classifiers, 300 datasets, and five STs demonstrate the feasibility and effectiveness of the approach. When using the ST selected by the Meta-scaler for each dataset, 10 of 12 base models tested achieved statistically significantly better classification performance than any fixed choice of a single ST. The Meta-scaler also outperforms state-of-the-art MtL approaches for ST selection. The source code, data, and results from the experiments in this article are available at a GitHub repository (https://github.com/amorimlb/meta_scaler).

Image Inpainting methods based on Neural Networks have shown an impressive improvement in the past few years, achieving ever more convincing results. However, these improvements have come at the cost of larger and more computationally intensive models, which demand powerful hardware for their execution. In this paper we introduce a high-quality and lightweight image inpainting architecture, based on LaMa’s frequency reconstruction network, using Unconstrained Blueprint Separable Convolutions, in combination with Relevant Channel Pruning and our technique for Residual Layer Pruning. By using Unconstrained Blueprint Separable Convolution, we manage to efficiently reduce the number of model weights by decorrelating the learned knowledge. We also observe that shallower residual layers within the network’s bottleneck are the main contribution to the quality of the final output of the network. Furthermore, we observe that weights with a larger L1 norm in the decoder represent the reconstruction of lower-frequency details in the image, such as color and coarse structures, while smaller L1 norm weights are responsible for high-frequency details in the image, such as edges and finer textures. By pruning these components in the network and performing fine-tuning on the remaining weights, the performance of the network can be improved while reducing the required resources. Our method can be applied to encoder-decoder architectures, and manages to reduce the model complexity while maintaining a competitive result compared to other models in the literature. The proposed architecture reduces by 72.89% the number of model parameters, and 65.80% in model FLOPs compared to LaMa, and compares favorably to state-of-the-art models when measuring FID and LPIPS of models of similar number of parameters and complexity.

Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the proposal of several DES approaches, no particular DES technique is the best choice for different problems. Thus, we hypothesize that selecting the best DES approach per query instance can lead to better accuracy. To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics. Experimental results show that using accuracy as a metric to select the ensembles, PS-DES performs better than individual DES techniques. PS-DES source code is available in a GitHub repository44https://github.com/prgc/ps-des.

The Android operating system provides functions and methods to handle sensitive data to secure users’ data. The Android security literature extracts binary features from a method and classifies the method into one of the Security Relevant Method's classes, adding information about how the method handles sensitive data. However, the usage of binary features hinders the performance of some classifiers due to the high collision rate between instances. Although previous works have explored Security Relevant Method classification, an extensive study of machine learning algorithms over this problem has not been conceived. This work fills this gap, analyzing Monolithic classifiers, Multiple Classifier Systems, and Embedding algorithms to transform binary features into real-valued features, aiming to facilitate the classifier's work by minimizing the ambiguity promoted by the collision. Our analyzes show that META-DES, using a pool of Decision Trees trained with the Random Forest algorithm, statistically has the best results. We also find that, in general, distance-based classifiers have a disadvantage in binary features. Moreover, embedding techniques such as deep metric learning with triplet loss can reduce geometrical instance ambiguity, improving the performance of the weakest learning algorithms. However, its usage was detrimental to the performance of more robust techniques, such as dynamic ensemble models better suited for handling difficult cases. The dataset and code used for the experiments are available in the following repository:https://github.com/walbermr/android-srm-ml-evaluation.

Risks related to excessive exposure of patients to ionizing radiation are a significant concern in the medical community. Several approaches based on Convolutional Neural Networks (CNNs) have been proposed to develop safer and more reliable sparse-view Computed Tomography (SVCT) systems. Most of those solutions process tomographic data within 2D slices individually. However, recent works have shown that 3D models - that exploit data correlation among adjacent slices - can outperform previous 2D models. Once the kernel size in most of those 3D models is not bigger than 5 x 5 x 5, such inter-slice exploration is restricted to a limited neighborhood, resulting in minor inter-slice analysis during the training/validation phase. To efficiently exploit data correlation among the coronal, axial, and sagittal views of the SVCT volume, we propose an ensemble of four 2D CNNs. Three of them are used to process the orthogonal SVCT volume views separately, and the fourth CNN combines the outputs from the previous three networks. Since our final architecture is highly deep, we also present a training method in stages to avoid the non-convergence of the deepest layers. We conducted experiments using head cone-beam Computed Tomography (CBCT) scans extensively used in imaged guided radiotherapy (IGTR) during brain tumor treatment. Our method presented superior results in reducing reconstruction artifacts of SVCT volumes compared to the state-of-the-art 2D and 3D models.

Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class. A promising approach among the ensemble-based methods in the context of imbalance learning is Dynamic Selection (DS). DS techniques single out a subset of the classifiers in the ensemble to label each given unknown sample according to their estimated competence in the area surrounding the query. Because only a small region is taken into account in the selection scheme, the global class disproportion may have less impact over the system's performance. However, the presence of local class overlap may severely hinder the DS techniques' performance over imbalanced distributions as it not only exacerbates the effects of the under-representation but also introduces ambiguous and possibly unreliable samples to the competence estimation process. Thus, in this work, we propose a DS technique which attempts to minimize the effects of the local class overlap during the classifier selection procedure. The proposed method iteratively removes from the target region the instance perceived as the hardest to classify until a classifier is deemed competent to label the query sample. The known samples are characterized using instance hardness measures that quantify the local class overlap. Experimental results show that the proposed technique can significantly outperform the baseline as well as several other DS techniques, suggesting its suitability for dealing with class under-representation and overlap. Furthermore, the proposed technique still yielded competitive results when using an under-sampled, less overlapped version of the labelled sets, specially over the problems with a high proportion of minority class samples in overlap areas. Code available at https://github.com/marianaasouza/lords.

Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by adopting ensembles of classifiers. In this approach, an instance is assigned as mislabeled if a high proportion of members in the pool misclassifies it. Previous authors have empirically evaluated this approach; nevertheless, they mostly assumed that label noise is generated completely at random in a dataset. This is a strong assumption since other types of label noise are feasible in practice and can influence noise detection results. This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent. In this setting, we investigate the effect of class distribution on noise detection performance since it changes the total noise level observed in a dataset under the NAR assumption. Further, an evaluation of the ensemble vote threshold is conducted to contrast with the most common approaches in the literature. In many performed experiments, choosing a noise generation model over another can lead to different results when considering aspects such as class imbalance and noise level ratio among different classes.

Dynamic selection systems work by selecting the most competent models from an ensemble. The key issue in these systems is to define the competence of the models. The models’ accuracy is commonly used to select the models or define the weights to be used in their combination. This competence is calculated using the feature space region, known as the region of competence, around the test pattern. The literature of dynamic classifier systems presents a good variety of competence measures, but some are not suitable for time series forecasting. However, some dynamic regression selection works present measures that can be used with time series forecasting problems. Such measures are extracted from the region of competence, and this work aims to evaluate these competence measures to calculate the weights of the models to combine them dynamically. The experiments are performed using three different machine learning algorithms with ten time series datasets and compare dynamic weighting algorithm, single model, and classic statistical combination techniques as Mean and Median. Our results show that the models’ combination performs better than the single model, Mean and Median, but the competence measure’s choice is time series and model-dependent.

Selecting the best architecture for a Deep Neural Network (DNN) is a non-trivial task since there is a massive amount of possible configurations (layers and parameters) and great difficulty in how to choose them. To make this task more independent of human interaction, this work addresses the DNN architecture selection problem as a multi-objective optimization task with different criteria in a combinatorial context. For this, we defined a new way to represent the architecture of DNN (layer sequence) as a solution in the optimization process. The proposed method attempts to find the best composition and sequence of layers for the DNN architecture satisfying two criteria: accuracy and F1score. The method was evaluated for performance and compared to the exhaustive and random approaches and state-of-the-art DNN algorithms. The results obtained showed that the proposed method is capable of achieving results close to the optimum, and competitive when compared to those results reached by state of the art algorithms.

Dynamic predictor selection has been applied to time series context to improve the accuracy to forecast. A crucial step in dynamic selection methods if the definition of the region of competence, which is composed of the most similar patterns to a test pattern, because the predictor that attains the best performance in this region is selected to forecast this test pattern. The performance of dynamic selection methods depends on two main parameters, the size of the region of competence and the similarity measure (also called of distance measure). This work evaluates the influence of these parameters on six real-world time series to forecasting one step. In the experiments, Bagging is adopted to generate a pool of predictors, where the best predictor is selected per query pattern based on its performance on the region of competence. The results show that the choice of an appropriate distance measure, as well as the size of the region of competence, is mandatory to boost the performance of the prediction system. Moreover, the results reinforce the importance of using a dynamic selection approach to improve forecasting accuracy when compared to the monolithic models, also called of single models.

Dynamic selection techniques aim at selecting the local experts around each test sample in particular for performing its classification. While generating the classifier on a local scope may make it easier for singling out the locally competent ones, as in the online local pool (OLP) technique, using the same base-classifier model in uneven distributions may restrict the local level of competence, since each region may have a data distribution that favors one model over the others. Thus, we propose in this work a problem-independent dynamic base-classifier model recommendation for the OLP technique, which uses information regarding the behavior of a portfolio of models over the samples of different problems to recommend one (or several) of them in a per-instance manner. Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant base-classifier models based on the local data complexity of the region surrounding each test sample. The OLP technique then produces a local pool with the model that yields the highest probability score of the meta-classifier. Experimental results show that different data distributions favored different model types on a local scope. Moreover, based on the performance of an ideal model type selector, it was observed that there is a clear advantage in choosing a relevant base-classifier model for each test instance in particular. Overall, the proposed model type recommender system yielded a statistically similar performance to the original OLP with fixed base-classifier model. However, the proposed framework struggled to recommend at least one relevant model type specially for the samples with low labelset cardinality. Given the novelty of the approach and the gap in performance between the proposed framework and the ideal selector, we regard this as a promising research direction. Code available at github.com/marianaasouza/dynamic-model-recommender.

Multiple Classifiers Systems (MCS) are based on the idea that the combination of the opinion of several experts can generate better results than when only one expert is used. Several MCS techniques have been developed; each one has its strengths and weaknesses depending on the context in which they are applied. This work presents a two-level sampling strategy for pruning methods that are applied to the credit scoring task. The first step of the proposal is to generate a pool using two well-known sampling methods, bagging and random subspace, that work complementarity in order to produce a diverse pool. After, a pruning method reduces the generated pool maintaining only the most competent classifiers. So, the proposal improves the MCS regarding the accuracy and the computational effort, since only a small percentage of the original pool is stored. The proposed architecture is evaluated in a credit scoring application, and the results showed that the proposed architecture obtained better accuracy rates than the single best approach and literature methods. These results were also obtained with ensembles whose sizes were around 20% of the original pools generated in the training phase.

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to predict the target value of a query pattern. This competence is calculated using the performance of the regressors in a local region of the feature space around the query pattern that is called the region of competence. Nonetheless, defining the correct measure to compute the degree of competence of the regressors is a hard task. In this work, we propose a new technique to DRS that selects the best competence measure for a given dataset. To validate our technique, we perform a set of comprehensive experiments on 15 regression datasets. The proposed technique can operate in three different fashions: (i) selection of the most competent regressor; (ii) combination of all regressors from the ensemble; and (iii) selection of a subset composed of the most competent ones and combine them. The proposals are compared against DRS algorithms, individual regressors, and static systems that use the Mean and the Median as a fusion strategy. The results show that the proposed technique, which chooses a different competence measure per task, outperforms literature techniques.

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.

Dynamic regressor selection (DRS) systems work by selecting the most competent regressors from an ensemble to estimate the target value of a given test pattern. This competence is usually quantified using the performance of the regressors in local regions of the feature space around the test pattern. However, choosing the best measure to calculate the level of competence correctly is not straightforward. The literature of dynamic classifier selection presents a wide variety of competence measures, which cannot be used or adapted for DRS. In this paper, we review eight measures used with regression problems, and adapt them to test the performance of the DRS algorithms found in the literature. Such measures are extracted from a local region of the feature space around the test pattern, called region of competence, therefore competence measures. To better compare the competence measures, we perform a set of comprehensive experiments of 15 regression datasets. Three DRS systems were compared against individual regressor and static systems that use the Mean and the Median to combine the outputs of the regressors from the ensemble. The DRS systems were assessed varying the competence measures. Our results show that DRS systems outperform individual regressors and static systems but the choice of the competence measure is problem-dependent.

Imbalanced problems are characterized by a disproportion between the number of samples from the classes in a classification problem. This difference in amount of examples may lead to a bias toward the majority class, hindering the recognition of the underrepresented minority class. Ensemble methods have been widely used for dealing with such problems, and have been shown to perform well on them. In this context, Dynamic Selection (DS) approaches, which perform the classification task on a local level, have been receiving some attention for their promising results. More specifically, the Frienemy Indecision Region Dynamic Ensemble Selection++ (FIRE-DES++) framework, which has yielded state-of-the-art results on imbalanced problems, use a data preprocessing technique for noise removal and a class-balanced neighborhood definition for coping with imbalanced datasets. A different DS-based approach proposed in a previous work, an online local pool generation method, generates on the fly locally accurate classifiers for labelling samples near the class borders. Though the local generation of the classifiers may reduce the impact of class imbalance on the performance of the technique, its suitability for imbalance learning was not yet evaluated. Thus, in this work we evaluate how well the online local pool generation method deals with imbalanced problems. We perform a comparative analysis with a baseline technique using three Dynamic Classifier Selection (DCS) techniques over 64 imbalanced datasets and four performance measures. We also evaluate the use of the preprocessing and balanced neighborhood definition steps from the FIRE-DES++ on the online scheme to assess their impact on the performance of the method. Moreover, we evaluate the online technique and its variants against seven state-of-the-art ensemble methods, including both static and DS approaches. Experimental results show that the approach of locally generating the classifiers is advantageous for imbalance learning, providing an improvement to the DCS techniques and yielding state-of-the-art results. Furthermore, the addition of the noise removal and the balanced neighborhood definition steps to the online scheme improved the overall results of the technique, which indicates the advantage of including such steps in DS-based techniques.

In this paper we evaluate the presence of anomalies in the Engine Coolant Temperature (ECT) sensor operation by collecting telemetry data of a single car in two different operational modes. The proposed approach has evaluated ten different one-class classifiers in three different anomaly levels, defined from the sensor's malfunctioning. Based on the results from the experimental data, the evaluation has shown: the One-Class Support Vector Machine with third- degree polynomial kernel function as the best anomaly detection technique for the vehicle operation in movement trajectory and the k-nearest neighbor as the best technique for the vehicle stopped, but with the engine running.

Class imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers has been reported to yield promising results. However, the majority of ensemble methods applied to imbalance learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of Dynamic Selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and 14 Dynamic Selection schemes. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the AUC and the [Formula: see text]-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.

Text categorization can be applied in many areas, such as Business Intelligence, sentiment analysis, social network monitoring, and security. Considering textual analysis techniques' wide applications, it is important to improve these techniques to obtain more precise and reliable results. Combined Dissimilarity Spaces (CoDiS) model implements a system for text categorization with multiple classifiers trained on different dissimilarity spaces, aiming to overcome Bag-of-Words' limitations. This study evaluates CoDiS performance when applying some parametric variations, such as the classifier, the data input format, and the dissimilarity measure. Macro F1 and Micro F1 are used to evaluate the performance, and the results show a significant difference among different configurations tested on CoDiS. Based on the statistical tests' results, it is possible to conclude that the best scenario is to apply TFIDF to represent the datasets, Cosine as the dissimilarity measure, and SVM as the classifier. This scenario also outperformed literature multiple classifier systems: Bagging and Random Subspace.

Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats. When the region of competence has samples of different classes, KNORAE can reduce the region of competence in such a way that only samples of a single class remain in the region of competence, leading to the selection of locally incompetent classifiers that classify all samples in the region of competence as being from the same class. In this paper, we propose two DES techniques: KNearest Oracles Borderline (KNORA-B) and K-Nearest Oracles Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E that reduces the region of competence but maintains at least one sample from each class that is in the original region of competence. KNORA-BI is a variation of KNORA-B for imbalance datasets that reduces the region of competence but maintains at least one minority class sample if there is any in the original region of competence. Experiments are conducted comparing the proposed techniques with 19 DES techniques from the literature using 40 datasets. The results show that the proposed techniques achieved interesting results, with KNORA-BI outperforming state-of-art techniques.

In time series analysis and forecasting, machine learning (ML) models have been widely used due to their flexibility and accuracy. However, the tuning process of their parameters is a hard task, mainly when complex time series are addressed. So, it is difficult to guarantee the optimal adjustment of the ML model parameters. This paper proposes a recursive approach based on the Perturbation theory to correct the forecasting of ML models. From the initial forecasting given by an ML model, a new ML model is trained using the error series (the difference between the actual series and forecasting) of the first model to decrease the overall error of the system. This process can be recursively repeated until convergence or some stop criterion. The response of the perturbative approach is composed of the sum of the predictions (perturbations) of the ML models trained in each recursion. The proposed approach is investigated with four ML models: Support Vector Regression, Multilayer Perceptron, Long Short-Term Memory, and Radial Basis Function network. The evaluation is performed with an experimental investigation conducted on four time series: Canadian Lynx, Sunspot, Star Brightness, and S&P500 index. The results show that the perturbative approach improves significantly the accuracy of all evaluated ML models.

In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems. Nevertheless, such techniques are still susceptible to the effects of noise and outliers in the training data. We propose a new method for the generation of pools of classifiers based on Bagging, in which the probability of an instance being selected during the resampling process is inversely proportional to its instance hardness, which can be understood as the likelihood of an instance being misclassified, regardless of the choice of classifier. The goal of the proposed method is to remove noisy data without sacrificing the hard instances which are likely to be found on class boundaries. We evaluate the performance of the method in nineteen public data sets, and compare it to the performance of the Bagging and Random Subspace algorithms. Our experiments show that in high noise scenarios the accuracy of our method is significantly better than that of Bagging.

In this paper, we propose a speaker segmentation method for meeting audio based on i-vector. The motivation is to utilize the Total Variability (TV) framework as a feature extractor and to exploit the potential of modeling the speaker and channel variabilities for speaker segmentation in meetings. A distance-based segmentation method is designed with the cosine distance. A sliding window with variable length searches for speaker turns, through the distance between the i-vectors extracted from two segments with the same size. The experiments are conducted on the AMI Meeting Corpus, covering several conversation scenarios. For the training data of the UBM and TV matrix, 5 conversations from AMI Meeting Corpus are sampled. Other 10 conversations from AMI Meeting Corpus to compose the test data. The experiments show an improvement in the MDR and FAR curves compared with the FixSlid approach with different distance metrics, and for most of the operating points when compared with the classical BIC based WinGrow. The proposed method has on average a better computational performance, improving in 61.5% compared with the XBIC based FixSlid, and improving in 86.7% compared with the BIC based WinGrow.

In this work, we investigate speaker-specific filter banks for text-independent speaker verification. The proposed method performs an heuristic search for the best filter-bank configuration using the Artificial Bee Colony (ABC) algorithm and a proper fitness function for the standard i-vectors/PLDA-based speaker verification system. Furthermore, filter-bank decorrelated amplitudes are used instead of the cepstral coefficients produced by Discrete Cosine Transform (DCT). In the experiments, the proposed method is compared to standard Mel and linear scales in both cases where the decorrelation is performed using DCT and high-pass filtering. The comparison is performed on the MIT Mobile Device Speaker Verification Corpus in a gender-dependent trial scheme. The proposed method outperformed the baseline systems in almost all the test sets for both genders. Performance gains of 4.6% and 26.0% are achieved for male and female speakers, respectively.

In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.

The Oracle model has been used not only for comparison between techniques but also in the design of different methods in Multiple Classifier Systems (MCS). Even though the model represents the ideal classifier selection scheme, Dynamic Classifier Selection (DCS) techniques present a large performance gap from the Oracle. This means that, for a significant number of instances, the DCS techniques are not able to select a competent classifier, despite the Oracles assurance of its presence in the pool. Given that issue, this work aims to investigate the reasons why the Oracle model may not be well suited for guiding the search for a promising pool of classifiers for DCS techniques. For this purpose, a pool generation method that guarantees an Oracle accuracy rate of 100% in the training set is proposed. This method is further used to analyse the behavior of DCS techniques when the presence of at least one competent classifier in the pool for each training sample is assured. Experiments show that integrating Oracle information in the generation phase of an MCS has little impact on the gap between the accuracy rates of DCS techniques and the Oracle. Moreover, it is also shown that, for a theoretical limit of 100%, the DCS techniques were only able to select a competent classifier for at most 85% of the instances, on average. Results suggest that the Oracle is not the best guide for generating a pool of classifiers for DCS, for the model is performed globally whilst DCS techniques work with local data only.

This paper describes a novel GMM-UBM based system that deals with the session noise variability problem. The system uses the Type-2 Fuzzy GMM framework by considering the speaker GMM parameters to be uncertain in an interval. The parameters intervals are estimated using a multicondition model training on noisy speeches that are synthesized from the speaker's utterances. Experiments were conducted using the MIT Device Speaker Verification Corpus with utterances having the lowest noise level as training data. The result shows an improvement in the EER of 24.11% for the proposed method compared to the GMM-UBM when evaluated over the noisiest utterances. This shows that the method reduces the effects of the session variability.

Dynamic selection (DS) is a mechanism to select one or an ensemble of competent classifiers from a pool of base classifiers, in order to classify a specific test sample. The size of this pool is user defined and yet crucial to control the computational complexity and performance of a DS. An appropriate pool size depends on the choice of base classifiers, the underlying DS method used, and more importantly, the characteristics of the given problem. After the DS method and the base classifiers are selected, an appropriate pool size for a given problem can be obtained by the repetitive application of the DS with a variety of sizes, after which a selection is performed. Since this brute force approach is computationally expensive, researchers usually set the pool size to a pre-specified value. However, this strategy may reduce the performance of the DS method. Instead, we propose a meta-regression model in order to predict a suitable pool size, based on the intrinsic classification complexity of a problem. In our strategy, we obtain the best pool sizes for a number of data sets, using the brute force approach. Additionally, we extract meta-features that represent classification complexity of a problem. These two pieces of information are associated by means of meta-regression models. Finally, for an unseen problem, we predict the pool size using this model and the classification complexity information.We carry out the experiments on 64 two-class data sets and with several well-known DS methods. We also consider variants of meta-regression techniques and report prediction results. We further analyze these results using a statistical test. Finally, we investigate the performance of a DS and observe that DS performs equivalently for predicted and the best pool sizes.

A common approach in text categorization is to represent each word as a feature, however, many of these features are irrelevant. So, dimensionality reduction is an important step to diminish the computational effort and to improve accuracy. This paper presents a filter method for feature selection called Category-dependent Maximum f Features per Document (cMFDR). cMFDR is an extension that improves the idea of the MFDR algorithm. In MFDR, the best features are selected exploring documents that overcome a threshold that is calculated for the whole dataset under evaluation. We show that having only one global threshold is not an optimal strategy since it disregards categories that contain few relevant features, impairing the classification precision. So, cMFDR computes one threshold per category to assure that every category contributes with a different number of features. Moreover, the threshold calculation is not biased by documents with large number of features, unlike MFDR. The experimental evaluation showed the effectiveness of cMFDR on four text categorization benchmarks using three feature evaluation functions and Naïve Bayes Multinomial classifier. cMFDR obtains better or similar results than MFDR in 98% of the cases.

In this paper, we propose a novel facial expression recognition method based on features of the motion, Facial Expression Recognition based on Motion Estimation (FERME). The proposed approach encodes the directional information of the facial expression. The facial motion is encoded by using the motion estimation between different images from the same (or similar) face. The facial expression image is compared against the most similar image from each facial expression of training database. The best match is obtained using the Structural Similarity Index (SSIM). We propose a modified version of the Adaptive Reduction Search Area algorithm (MARSA) for motion vector calculation. FERME compares the motion vectors to the vectors of the highest occurrences obtained from each facial expression. From this comparison, the Euclidean distances vectors are generated. Support Vector Machine (SVM) is used to classify the facial expression. The experimental results show the effectiveness of the proposed approach.

In smart environments, the extraction of relevant information in large volumes of data collected from intelligent devices is a crucial issue. The extracted information can assist in automation of user activities and on daily chores, either suggesting or even changing the state of devices based on his/her routine. In this work, we propose a prediction architecture which combines an innovative preprocessing strategy with some well known classification algorithms for the environment automation. The preprocessing enhances the datasets by including features and organizing them in structures that improve the classification results. We verify which preprocessing parameters have significant impact on prediction performance using datasets collected from a real home equipped with sensors. In simulations, the avNNet, mlp and C5.0 classifiers attained the higher accuracies using Friedman and Nemenyi statistical tests, but none of them outperformed the others in all scenarios using this architecture.

Supervised Fractional Eigenfaces (SFE) is an extension of Principal Component Analysis (PCA), which uses the fractional covariance matrix, class label information, and nonlinear data transformation to extract discriminant features. The proposed method combines techniques of two state-of-the-art feature extractors: Fractional Eigenfaces and Dual Supervised PCA. Supervised Fractional Eigenfaces was evaluated in three known face datasets and it achieved significant smaller recognition error.

This paper presents three new efficient 2×2 block-based algorithms for connected components labeling: a two-scan which assigns provisional labels to blocks, a two-scan which assigns provisional labels to pixels and a one-and-a-half-scan which assigns provisional labels to blocks. A new stripe image representation is designed in order to perform the second pass only through the blocks containing some foreground pixel. We also improved the existing 2×2 block-based algorithms by utilizing information of a pixel during a transition in the mask, which allows checking of four neighbor pixels in the mask at most. Thus, the average number of checking operations needed to inspect the neighbor pixels in the first scan is reduced from 1.459 to 1.156, an improvement of 21%. We conducted experiments using synthetic and real images to evaluate the performance of the proposed methods compared to the existing methods. The proposed block-based one-and-a-half-scan algorithm presents the best performance in the real images dataset, which is composed of 1290 documents. Our block-based two-scan algorithm which assigns provisional labels to pixels showed to be the fastest in the synthetic dataset, especially in high density images.

In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic ensemble selection framework using meta-learning, called META-DES. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. In the second phase the meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance. In this paper, we propose improvements to the training and generalization phase of the META-DES framework. In the training phase, we evaluate four different algorithms for the training of the meta-classifier. For the generalization phase, three combination approaches are evaluated: Dynamic selection, where only the classifiers that attain a certain competence level are selected; Dynamic weighting, where the meta-classifier estimates the competence of each classifier in the pool, and the outputs of all classifiers in the pool are weighted based on their level of competence; and a hybrid approach, in which first an ensemble with the most competent classifiers is selected, after which the weights of the selected classifiers are estimated in order to be used in a weighted majority voting scheme. Experiments are carried out on 30 classification datasets. Experimental results demonstrate that the changes proposed in this paper significantly improve the recognition accuracy of the system in several datasets.

The nearest neighbor (NN) is one of the most well known classifiers in pattern recognition. Despite the high classification accuracy, the NN has several drawbacks: high storage requirements, bad time of response, and high noise sensitivity. Prototype Generation (PG) is one of the most well-known solutions to tackle these shortcomings. In supervised classification, many real world datasets do not have an equitable distribution among the different classes, these are called imbalanced datasets. Many PG techniques that have a high classification accuracy in regular datasets, have a poor performance when dealing with imbalanced datasets. The Self-Generating Prototypes (SGP) is one of these techniques. The Adaptive Self-Generating Prototypes was proposed to tackle the SGP problem with imbalanced datasets, but, in doing so, the reduction rate is compromised. This paper proposes the Evolutionary Adaptive Self-Generating Prototypes (EASGP), a SGP based technique with iterative merging and evolutionary pruning to help find the optimal solution. An experimental analysis is performed with datasets of different levels of imbalance ratio and statistical tests are used to evaluate the proposed technique. The results obtained show that EASGP outperforms previous SGP based algorithms in classification accuracy and reduction.

We propose a bootstrap-based iterative method for generating classifier ensembles called Iterative Classifier Selection Bagging (ICS-Bagging). Each iteration of ICS-Bagging has two phases: i) bootstrap sampling to generate a pool of classifiers; and, ii) selection of the best classifier of the pool using a fitness function based on the ensemble accuracy and diversity. The selected classifier is added to the final ensemble. The bootstrap sampling runs on each iteration and updates the probability of sampling per class based on the class accuracy. This process is repeated until the number of classifiers in the final ensemble is reached. For the specific case of imbalanced datasets, we also propose the SMOTE-ICS-Bagging, a variation of the ICS-Bagging that runs SMOTE at the beginning of each iteration in order to reduce the class imbalance before data sampling. We compared the proposed techniques with Bagging, Random Subspace and SMOTEBagging, using 15 imbalanced datasets from KEEL. The results show the proposed techniques outperform all other techniques in accuracy. Ranking diagrams revealed that the proposed algorithms achieved the highest rankings in accuracy, outperforming SMOTEBagging, a renowned ensemble generation method for imbalanced datasets.

The proposed Fractional Eigenfaces method is a feature extraction technique for high dimensional data. It is related to Fractional PCA (FPCA), which is based on the theory of fractional covariance matrix, and it is an extension of the classical Eigenfaces. Like FPCA, it computes projections for a low dimensional space from the fractional covariance matrix and similar to the Eigenfaces, it is suited for high dimensional data. Moreover, the proposed technique extends the fractional transformation of the data for more stages of the feature extractions than FPCA. The Fractional Eigenfaces is evaluated in three different face databases. Results show that it achieves a higher accuracy rate than FPCA and Eigenfaces according to the Wilcoxon hypothesis test.

In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of a given query sample. The meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance. Three different training scenarios for the training of the meta-classifier are considered: problem-dependent, problem-independent and hybrid. Experimental results show that the problem-dependent scenario provides the best result. In addition, the performance of the problem-dependent scenario is strongly correlated with the recognition rate of the system. A comparison with state-of-the-art techniques shows that the proposed-dependent approach outperforms current dynamic ensemble selection techniques.

This paper proposes the use of the type-2 fuzzy GMM (T2FGMM) framework in order to improve the verification rates of the standard GMM-UBM text-independent speaker verification system in noisy environments. Based on type-2 fuzzy sets, the T2FGMM framework describes GMMs with uncertain parameters and provides likelihood intervals for them. The proposed method (T2F-GMM-UBM) estimates the parameter intervals using the noisy speeches from the speakers and the Bayesian estimation used in the standard GMM-UBM system. The proposed method was evaluated using the MIT Device Speaker Verification Corpus (MITDSVC) which contains speeches from 48 speakers recorded in three different locations: a quiet office, a mildly noisy lobby, and a busy street intersection. The Equal Error Rate (EER) was computed for each speaker and the mean and standard deviation were analyzed. Although the proposed method did not achieve better performance in the office location, significant improvements were achieved in both lobby and street intersection locations. The improvement in the lobby was 14.21% while in the street intersection location was 10.47%. The left tailed paired Rank Sign Wilcox on Test was also performed in both locations and the p-values found were 0.0127 and 0.0230, respectively. The proposed method proved to have better performance in noisy environments compared to the standard GMM-UBM system.

Modular Neural Network (MNN) divides a problem into smaller and easier sub-problems, and each sub-problem is solved by a neural network called expert. In previous MNN architectures, all experts used the same set of features. This work proposes a modular neural network architecture in which a specialized set of features is selected per expert. As each expert deals with a different sub-problem, it is expected an improvement in the accuracy rate when different and specialized features are selected per expert. The feature selection procedure is an optimization method based on the binary particle swarm optimization. Experimental results over public datasets show that the proposed modular neural network obtains better accuracy rates than literature MNNs.

Many instance selection methods (ISMs) have been widely studied and proposed. But none of these methods obtain good performance on every data set. In this work, we propose an architecture to select the best ISM for a given data set. We use meta-learning to train a meta-classifier that learns the relationship between the ISMs performance and the data set structure. The proposed method was evaluated on public data sets and showed better results than traditional approaches.

Block-based algorithms are considered the fastest approach to label connected components in binary images. However, the existing algorithms are two-scan which would need more comparisons if they were used as one-and-a-half-scan algorithms. Here, we proposed a new mask that enables the design of a block-based one-and-a-half-scan algorithm without any extra comparison. Furthermore, three new efficient algorithms for connected components labeling are presented: a block-based two-scan, a pixel-based one-and-a-half-scan and a block-based one-and-a-half-scan. We conducted experiments using synthetic and realistic images to evaluate the performance of the proposed methods compared to the existing methods. The proposed block-based one-and-a-half-scan algorithm presents the best performance in the realistic images dataset composed of 1290 documents. Our block-based two-scan algorithm proved to be the fastest in the synthetic dataset, especially in low density images.

Image segmentation is one of the basic steps in image analysis. Clustering methods are an unsupervised way to provide image segmentation. This paper proposes a clustering algorithm for contextual image segmentation, called spatially variant finite mixture model (SVFMM). For the case of spatially varying mixture of Gaussian density functions with unknown means and variances, an expectation-maximization (EM) algorithm is derived for maximum likelihood estimation of the parameters of the mixture model. In this paper, the Potts model is adopted as a priori density function for the spatially variant mixture proportions to imposes spatial smoothness constraints in the model. Experimental results on a set of different real images show the effectiveness of the proposed method.

This work proposes an optimization technique based on binary particle swarm optimization that performs feature selection and feature weighting simultaneously. In the optimization process, each member of the population is described as a vector having three parts: i) one weight per feature (feature weighting), ii) one binary value per feature indicating the presence or the absence of the feature (feature selection), and, iii) the number of neighbors of the kNN classifier. After optimization, this vector is used as a mask to generate a new subset of features that is evaluated using the kNN classifier. The experimental study was performed on public datasets and showed that the proposed technique obtains better accuracy and reduction rates than state-of-the-art techniques.

This work presents a complete method for improving the handwritten document recognition. In this task some characters are confused with others because of their visual/structural similarity. A SOM and TreeSOM neural network were used to sort different characters in metaclasses. In each metaclass a zoning approach was applied trying to get particular features to improve the character classification. The experiments with this new approach were performed in the NIST database with the classic MLP and a fast neural network RBF-DDA.

The “no free lunch” theorem has stated that learning algorithms cannot be universally good. An alternative to alleviate the weakness of using only one classifier is to combine several of them. Mixture of Experts is a learning algorithm that combines classifiers, in which each classifier or expert is dedicated to solve part of the problem. The partition of the problem is defined by a step called Task Decomposition where the problem is divided in subproblems. This paper proposes an approach to combine mixture of experts, in which different task decomposition methods are used to divide the problem. This strategy aims to increase the diversity of the ensemble, since different task decomposition methods generate different partitions of the database. The experimental study shows that the proposed method obtains better accuracy rates when compared with the traditional mixture of experts.

We present a combined features approach for speaker segmentation task. This approach utilizes different acoustic features extracted from audio stream. The Bayesian Information Criterion (BIC) is used for each acoustic feature as a distance measure to verify the merging of two audio segments. An Artificial Neural Network (ANN) combines the time index from each ?BIC with the highest value, and estimates the change point. In the experiments, a data set containing examples with several speakers is used to compare our approach with the Chen and Gopalakrishnan's window-growing-based approach, using different acoustic features sets. The results show an improvement in both the Miss Detection Rate (MDR) and the False Alarm Rate (FAR) compared to the window-growing-based approach.

This paper proposes the use of a type-2 fuzzy framework in the standard GMM-UBM based text-independent speaker verification systems. Based on type-2 fuzzy sets, the framework provides pertinence intervals for the models. The decision process is obtained using a Support Vectors Machine (SVM) that processes the interval likelihoods. A Voice Activity Detection (VAD) algorithm was also used to discard the parts of the speech signal without voice. The proposed method was tested on the MIT Device Speaker Verification Corpus which contains several different mobile devices used in different environments. The result shows the robustness of the system and the improvements in the verification ratios of the T2F-GMM-UBM compared to the classical GMM-UBM based systems.

This paper presents a motion compensation technique applied in the permutation-based digital video encryption and compression method introduced by Socek et al. The encryption method is based in permutations that can improve the spatial correlation on each video frame, making them more compressible by a spatial encoder. However, the compression performance of this method depends on the temporal correlation between consecutive frames and the algorithm does not provide a way to explore non-trivial temporal correlation properly. Consequently, the compression ratio of an encrypted video is very sensitive to the motion in the scene. Here, we propose a motion compensation method to be applied in both encryption and decryption process. Experiments using the H.264 codec show a significant improvement of the compression performance in high motion video sequences.

Pedestrian detection is a very promising area in computer vision, since it enables interesting and a variety of applications such as car assistance, surveillance systems and robot vision. During the last years, a variety of new techniques were proposed which greatly improved the detection rates. However, the performance of such systems rapidly deteriorates when pedestrians are under occlusion. This paper analyze how the detection rates of HOG, HOG-LBP, and two new combinations, HOG-LTP and HOG-LMEBP, are affected when occlusion area are progressively added to pedestrian images. Using the INRIA dataset, occlusions were synthetically generated by merging different sizes of non-pedestrian images from different directions. We show that detection of pedestrian under occlusion can be improved by simply combining features.

The human visual system is one of the most fascinating and complex mechanisms of the central nervous system that enables our capacity to see. It is through the visual system that we are able to accomplish from the most simple task such as object recognition to the most complex visual interpretation, understanding and perception. Inspired by this sophisticated system, two models based on the properties of the human visual system are proposed. These models are designed based on the concepts of receptive and inhibitory fields. The first model is a pyramidal neural network with lateral inhibition, called lateral inhibition pyramidal neural network. The second proposed model is a supervised image segmentation system, called segmentation and classification based on receptive fields. This work shows that the combination of these two models is beneficial, and the results obtained are better than that of other state-of-the-art methods.

This paper proposes a metric learning algorithm based on the L2-Norm (L2ML) in the context of the face pair-matching problem as an attempt to overcome the low discriminatory power of most current descriptors when the operating conditions are unconstrained. The L2ML differs from other similar techniques by giving an efficient closed-form solution to a relatively simple optimization objective. As the experiments show, despite the simplicity, the performance of the proposed method is comparable to that of more complex state of the art techniques. In fact, the combination of only two descriptors in the L2ML space reaches an average accuracy of 84.97% in the challenging Image Restricted benchmark of the aligned Labeled Faces in the Wild (LFW) dataset.

The segmentation of blood vessels in retinal images is an important procedure for the prediction and diagnosis of cardiovascular diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work aims to develop an effective method of retinal vessels segmentation by combining correlation filters and measures extracted from the eigenvalues of the Hessian matrix. The approach uses a threshold to segment the image generated by this combination and is evaluated on two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.

Image focus is a property closely related to image quality. In some images it is not possible to obtain a clear focus in all regions simultaneously, so an alternative is to use image fusion to combine pictures with different focus into one with all the best-focused regions. This paper describes two image fusion algorithms in the frequency domain that are based on focus: Contrast in DCT domain and Spatial Frequency. The algorithms divide the images in fixed size blocks to decide which image should be selected to constitute the final result. Improvements are made to both techniques to decide when to choose an entire block or pixels individually. The proposed approach combines the different techniques (or different settings of a single technique), by comparing evaluation metrics (PSNR) values obtained for each block independently and selecting the technique that performs better for the analyzed block. The final image quality, evaluated using PSNR and RMSE, is superior compared to the results of the individual techniques.

The classifier accuracy is affected by the properties of the data sets used to train it. Nearest neighbor classifiers are known for being simple and accurate in several domains, but their behavior is strongly dependent on data complexity. On the other hand, there are data complexity measures which aim to describe properties of the data sets. This work aims to show how data complexity measures can be efficiently used to predict the behavior of the Nearest Neighbor classifier. Seven data complexity measures and seventeen real datasets are used in the experimental study. Each data complexity measure is analyzed individually in order to find a relationship between its value and the accuracy of the classifier on a given dataset. No single measure used is good enough to predict the behavior of the Nearest Neighbor classifier. However, the combination of these measures provides a powerful tool to predict the accuracy of the Nearest Neighbor classifier.

In this paper, we present an efficient method for facial expression recognition. Three features extraction methods are combined to form a model representation for facial expressions. Once the feature and the model representation are defined a Support Vector Machine (SVM) is used for the classification task. The proposed method is tested using the Yale and Cohn-Kanade databases, which contains 165 images and 1480 images, respectively. The method presented a recognition rate of 98.1% and 93% for the Yale and Cohn-Kanade respectively.

Some real world datasets have different proportions of classes, too many instances of the majority classes and only a few of the minority classes, those are called imbalanced datasets. Many applications, like medical diagnosis and risk analysis, are interested in the under-represented class, but classifiers and prototype generation techniques usually have a bias towards the majority classes. Because of that, the problem of classification with imbalanced datasets has become an important topic in Pattern Recognition. The Self-Generating Prototypes (SGP) have a high reduction power and an excellent performance with balanced datasets, but, with imbalanced datasets, the generated prototypes do not have a good representation of the training dataset. This algorithm generates many prototypes of the majority classes and only a few, or even none, of the minority classes. The aim of this paper is to propose the Adaptive Self-Generating Prototypes (ASGP), an improvement of the SGP2, the second version of the SGP, designed to handle imbalanced datasets. This paper also exposes the reasons for the low performance of the SGP2 with such datasets. Empirical results show that the ASGP has a higher performance with imbalanced datasets than the SGP2, especially when it comes to classification accuracy of the minority classes.

Image segmentation of retinal blood vessels is an important procedure for the prediction and diagnosis of cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work develops an unsupervised segmentation procedure for the segmentation of retinal vessels images using a combined matched filter, Frangi filter and Gabor Wavelet Filter. After the vessel enhancement, two segmentation methods are tested. The first method uses an approach based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.

This paper introduces a novel prototype selection scheme that decides which instances to preserve using an approach that defines an order to the instances in the data sets. The order of each instance is defined by its relevance to the data set considering the similarity to their nearest eighboors. Scores are assigned to the instances. Instances surrounded by others of the same class have highest scores and have priority in the selection. Experiments performed over several classification problems show that the proposed method reduces the storage requirements and keeps or improves the classification accuracy.

This paper proposes a method for linear feature extraction called Class-dependent Locality Preserving Projections. It is a supervised extension of the Locality Preserving Projection algorithm and it aims to work in scenarios with within-class multimodality, which are those scenarios where the scattering of the patterns follows more than one modal distribution. Differently from the classical feature extraction techniques that build their solutions based on the whole dataset, the Class-dependent Locality Preserving Projections looks at each class separately, building a specific projection for each class. The proposed technique analyses a query pattern based on the output of each class and chooses the class that better fit the pattern. The experimental study shows that the Class-dependent Locality Preserving Projections is a feature extraction technique for general purposes, however, it is particularly well succeed when applied to within-class multimodal scenarios.

A modular neural network architecture is composed by independent neural networks that focus on different parts of the whole task. This work proposes the Intrinsic Modular Neural Networks that aims not only to reduce the number of classes and patterns in each independent neural network, but also to reduce the dimensionality of the data. The task decomposition is performed by the High-Dimensional Data Clustering algorithm. After the clustering, the training patterns are divided in groups and each group is used to train an independent neural network. Experiments on public databases show promising results.

Artificial Neural Networks (ANNs) has been applied in the face detection task because of its ability to capture the complex probability distribution conditioned to the class of face patterns. However, many works use Back-Propagation (BP) to adapt the weights of the ANNs. The problem of using BP is that it has many disadvantages related to the appropriate choice of its parameters, as the learning rate and momentum. Furthermore, since BP assumes a fixed architecture for the ANN, an inappropriate choice of the architecture can make it have a sub-optimal performance. In this paper we investigate the application of the IPSONet in the facial detection task. IPSONet is a training technique for neural networks like multilayer perceptron (MLP) that uses an improved PSO to evolve simultaneously structure and weights of ANNs. Thus, the IPSONet produces ANNs with higher generalization ability if compared to BP. The system developed in this work, which includes the feature extraction process of the input image and the training of a MLP net using IPSONet is called IPSONetFD. The experiments using the MIT CBCL Face Database showed that the proposed technique is robust in the sense that it can detect faces with a wide variety of pose, lighting and face expression. The results showed that the IPSONetFD had better performance than others ANN's architectures (PyraNet and I-PyraNet, in this study), and an equivalent performance if compared to SVM. Thus, the proposed technique demonstrated that ANNs trained by IPSONet has better performance than ANNs trained by BP in the face detection task.

We introduce a fingerprint spoof detection technique based on MLP and SVM that combines several features. The proposed technique is evaluated on two scenarios: (i) when an impostor can perform consecutive attempts to be considered authentic; and, (ii) when the system deals with fingerprints from elderly people. In order to analyze these scenarios, a database was developed. The results show that the proposed combination of features increases the system performance in at least 33.56% and that the average error increases as more attempts for acceptance are allowed. The SVM classifier presents better performance in almost all the tested configurations. However, MLP is more accurate with biometrics from elderly people.

Segmentation is a preliminary step for many computer vision systems. Several segmentation algorithms have been developed for different tasks. Here, we are interested in the pupil segmentation, an important procedure in iris recognition systems. In most of the pupil segmentation algorithms it is assumed that the pupil has a circular shape. These methods inaccurate identify pupil borders that do not have a circular shape. In iris recognition, the error caused by an imprecise segmentation can lead to poor recognition rates. In this paper we propose a new method for pupil segmentation based on the Pulling & Pushing method and a batch-SOM neural network in order to improve the segmentation. We tested the proposed method in the MMU1 and Casia V3 iris databases, obtaining accurate results.

Neighborhood coding is a binary image representation method that has been performing successfully for a variety of applications such as features extraction for image recognition, shape descriptor, and image compression. Despite the success of this coding method, the representation lacked a formal notation. Here, we proposed a formal mathematical notation to represent any binary image into a neighborhood coding scheme. Using this representation we also introduce the concept of neighborhood operations, a procedure akin to mathematical morphology, however having a lower computational cost.

Face detection is a research area in computer vision of great interest. Even though several different methods have been developed, improvements can still be made in the false-positive detection and increase in the speed of the detector. In this work, we investigate the AdaBoost technique as an artificial neural network. We propose a new model called MLPBoost, which is an hybridization between AdaBoost and Multi-Layer Perceptron (MLP) networks. This algorithm has shown improvements in the performance of classifiers already trained with AdaBoost, either by the increase in the detection rate and the reduction of false positive rates, or by decreasing the processing time of these classifiers.

This work proposes an improvement for fingerprint spoof detection in order to reduce the occurrence of live fingerprints from elderly people taken as spoof. The novel architecture combines classifiers working independently into two distinct image quality groups. The results show that high quality spoof images are easily detected, the misclassification rate for live fingerprints is reduced by 64.0% and the overall system performance increases 49.61%.

Currently one of the most important challenges of face recognition systems is the problem of occlusion, which is quite common in real applications. There are several studies in the literature treating this problem, but no defined or robust solution is agreed. The focus of this work is to develop face recognition method with sunglasses and scarf occlusion. We propose a robust approach which consists in detecting the face region that does not have occlusion and uses this region to obtain the recognition. To classify the occluded and non-occluded parts, a Multi-Layer Perceptron (MLP) is applied. While for the recognition a combined Gradientface and Local Binary Pattern (LBP) are used. Gradientface is applied to address the variation in the illumination of the image. Experiments are shown using the AR Face and ORL databases.

This paper proposes the use of a Type-2 Fuzzy GMM (T2FGMM) speaker verification system for mobile devices in variable environments. This model is an extension of Gaussian mixture models based on the type-2 fuzzy set, which provide pertinence intervals for the trained samples. The decision process is obtained using the Generalized Linear Model (GLM) that processes the interval likelihoods. A Voice Activity Detection (VAD) algorithm was also used to improve the speaker verification ratio. The proposed method was tested on the MIT mobile device speaker verification database which contains several different mobile devices used in different environments. The result shows the robustness of the system and the improvements in the verification ratios of the T2GMM over the classical GMM.

The growing amount of information transferred and stored in graphical and video format, and the forthcoming of new devices that have a limited hardware capabilities to either store data or display shades (such as electronic paper), creates a demand for higher computational resources or new techniques that consume less of these resources. In this paper is proposed a new method for video coding called Video Colortoning, which reduces the size of the image representation in a sequence of color image, with a small loss of quality and in real time. The proposed method treats the problem of quantization noise and the flicker effect, optimizes the data for further compression and considers the human visual system regarding the handling of colors.

Regardless of several different methods for face detection have been developed in the last years, there are still situations that requires more improvements especially in issues related to variations in illumination and face occlusion. Illumination problems are normally handled by using preprocessing, and model or training-based approaches. We propose here a face detection method combining the well-known AdaBoost with Gradientfaces following a model-based approach, which was not yet used for the face detection problem. We have applied Gradientfaces before training an AdaBoost Haar-based cascade classifier to overcome the problem of strong variations in illumination. Cited approaches were evaluated first in a data set containing artificial and then real illumination problems. Experiments show that proposed method is stable when facing different lighting conditions, and better than others when dealing with strong and uncontrolled illumination problems.

This paper presents a Fuzzy Active Contour Model for image segmentation using three variations. The proposed models are based on the Fuzzy Energy-Based Active Contour model introduced by Krinidis and Chatzis. First, an update criteria that changes only localized membership values at each iteration is introduced. Second, the model is extended to a type-2 fuzzy logic. And finally, a multiple object segmentation schema is applied to the original model. We present some experimental results, showing the performance for each modification and some of its advantages.

Face verification systems reach good performance on ideal environmental conditions. Conversely, they are very sensitive to non-controlled environments. This work proposes the class-Modular Image Principal Component Analysis (cMIMPCA) algorithm for face verification. It extracts local and global information of the user faces aiming to reduce the effects caused by illumination, facial expression and head pose changes. Experimental results performed over three well-known face databases showed that cMIMPCA obtains promising results for the face verification task.

Segmentation is a fundamental step in the process of handwritten digits recognition. However, it is common to have images with connected digits after the segmentation task and this affects the classifier accuracy. This paper presents an approach for handwritten connected digits classification based on instance selection. The new technique uses information from all data of the training set to build a ranking of the instances. The instances having the highest scores are chosen to represent the data points of the problem. A set of features especially designed for the problem is extracted. The experimental study using a real world database shows that the proposed technique is quite efficient in the detection of handwritten connected digits.

Pedestrian detection is a task usually associated with security and surveillance systems. The development of a pedestrian detection system poses a hard challenge, because of its inherently complex nature. In this work, we present an analysis of an existing pedestrian detection model based on PCA reconstruction errors. We investigate how the method works and where changes can be made to improve its original performance. The proposed improvements enhance the system's accuracy by using weights, that are found in an automated way using a genetic algorithm. We also found that some reconstruction errors used by the original method are not strictly necessary and therefore they can be eliminated to reduce the classifying time by half.

Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult the classifier selection. This fact makes the performance of most dynamic selection systems no better than static selections. In this paper we demonstrate that the performance of dynamic selection systems end up limited by the quality of the regions extracted. Thereafter, we propose a new dynamic classifier selection system that improves the regions of competence in order to achieve higher recognition rates. Results obtained from several classification databased show the proposed method not only significantly increase the recognition performance, but also decreases the computational cost.

In this paper, the face verification problem is addressed. A neural network with autoassociation memory and receptive fields based architecture is proposed. It is called AAPNet (AutoAssociative Pyramidal Neural Network). The proposed neural network integrates feature extraction and image reconstruction in the same structure. For a given recognition task, at least one instance of the AAPNet must be trained for each known class. Thus, the AAPNet outputs how similar is a given probe image to its class. The AAPNet is applied in a face verification task using thumbnail-sized faces and achieves better results when compared to state-of-the-art models.

A novel framework for time series prediction that integrates Genetic Algorithm (GA), Partial Axis Search Tree (PAT) and K-Nearest Neighbors (KNN) is proposed. This methodology is based on the information obtained from Technical analysis of a stock. Experiments have shown that GAs can capture the most relevant variables and improve the accuracy of predicting the direction of daily change in a stock price index. A comparison with other models shows the advantage of the proposed framework.

In this paper, a simulation of intelligent agents is developed to recreate the environment of negotiation of stock markets. The focus is analyze the behavior of movement/ fluctuation of stock markets. This movement can be captured by a measure called volatility, which is the difference between two stock prices in distinct periods. It characterizes the sensibility of a market change in the world economy. The contributions of this work are three-fold: (i) a simulation of dynamics of stock markets based in intelligent agents; (ii) based in this simulation an analysis of the volatility dynamic of the simulated time series; (iii) after that, a investigation about the relationship between the volatility of the markets, distribution of gain/loss money of agents and the coefficient of the exponential function based on the ideal gas theory of Maxwell-Boltzmann. This information can be used, for example, to predict the future behavior of the markets.

This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.

The time series forecasting is an useful application for many areas of knowledge such as biology, economics, climatology, biology, among others. A very important step for time series prediction is the correct selection of the past observations (lags). This paper uses a new algorithm based in swarm of particles to feature selection on time series, the algorithm used was Frankenstein's Particle Swarm Optimization (FPSO). Many forms of filters and wrappers were proposed to feature selection, but these approaches have their limitations in relation to properties of the data set, such as size and whether they are linear or not. Optimization algorithms, such as FPSO, make no assumption about the data and converge faster. Hence, the FPSO may to find a good set of lags for time series forecasting and produce most accurate forecastings. Two prediction models were used: Multilayer Perceptron neural network (MLP) and Support Vector Regression (SVR). The results show that the approach improved previous results and that the forecasting using SVR produced best results, moreover its showed that the feature selection with FPSO was better than the features selection with original Particle Swarm Optimization.

Segmentation is a preliminary step in many computer vision systems. In most of pupil segmentation algorithms it is assumed that the pupil has a predefined shape, usually circular. This parametrization might lead to errors when the eye image is distorted or deformed and when the pupil is partially occluded by eyelids or eyelashes. In this work, we propose a new method for pupil segmentation based on a batch-SOM (BSOM) neural network composed by three steps: (1) definition of the initial neurons position; (2) use BSOM to extract the contour; and (3) perform a contour adjustment. The method is capable of finding the pupil contour in a flexible manner, independently of a predefined shape. We modified the BSOM algorithm in three points: (1) in the update process, introducing the neighborhood constraint; (2) removal of the neurons, and (3) in the convergence criteria. Experiments were performed using Casia-IrisV3 Interval, Casia-IrisV4 Syn, and MMU1 iris image databases.

A social network is composed by communities of individuals or organizations that are connected by a common interest. Online social networking sites like Twitter, Facebook and Orkut are among the most visited sites in the Internet. Presently, there is a great interest in trying to understand the complexities of this type of network from both theoretical and applied point of view. The understanding of these social network graphs is important to improve the current social network systems, and also to develop new applications. Here, we propose a friend recommendation system for social network based on the topology of the network graphs. The topology of network that connects a user to his friends is examined and a local social network called Oro-Aro is used in the experiments. We developed an algorithm that analyses the sub-graph composed by a user and all the others connected people separately by three degree of separation. However, only users separated by two degree of separation are candidates to be suggested as a friend. The algorithm uses the patterns defined by their connections to find those users who have similar behavior as the root user. The recommendation mechanism was developed based on the characterization and analyses of the network formed by the user's friends and friends-of-friends (FOF).

Pupil segmentation is usually the first step used for searching iris regions. Iris localization is an extremely important procedure in iris biometrics systems, since the correct segmentation of inner and outer boundaries is critical to achieve high recognition rates. An iris localization method based on a spring force-driven iterative scheme, called Pulling & Pushing have been proposed by He et al. 2006. Here, we propose a pupil segmentation procedure that combines Pulling & Pushing and Active Contour Models, overcoming and improving the results of the previous method. We also developed a new strategy to identify and fill reflection points that appear inside the pupil. We tested our method in MMU1 and Casia V1 and V3 iris databases, obtaining accurate results.

Neighborhood coding was proposed to encode binary images. Previously, this coding scheme presented good results in the problem of handwritten character recognition. In this article, we extended this coding scheme so that it can be applied as an image shape descriptor and in a bilevel image compression method. An algorithm to reduce the number of codes needed to reconstruct the image without loss of information is presented. Using the exactly same set of reduced codes, a lossless compression method and a shape recognition system are proposed. The reduced codes are used with Huffman coding and RLE (Run-Length Encoding) to obtain a compression rate comparable to well-known image compression algorithms such as LZW and CCITT Group 4. For the shape recognition task we applied a template matching algorithm to the set of strings generated by the coding reduction procedure. We tested this method in the MPEG-7 Core Experiment Shape 1 part A2 and the binary image compression challenge database.

This paper presents an analysis of the parameters used to construct 2D log-Gabor filters to encode iris patterns. This filter is a band-pass complex filter composed by four parameters that are used to extract information direct in the 2D domain. An iris recognition system, composed by segmentation, normalization, encoding and matching is also described. The segmentation module combines the Pulling & Pushing and Active Contour Model and the Circular Hough Transform to find the inner and the outer boundaries of the iris. The experiments were performed using the CASIA v1 iris database and the results are analyzed using ROC curves. We conclude that 2D log-Gabor filters are also an effective alternative to encode the features present on iris patterns. The combination of the modified segmentation procedure and the use of 2D Log-Gabor filters showed good results for certain important regions of the ROC curves.

Instance-based learning algorithms typically suffer influences of dissimilarity functions. The problem is frequently related to the Nearest Neighbor rules of these algorithms. This paper will introduce a new dissimilarity measure, called Heterogeneous Centered Difference Measure, which is tested over many known databases. The results are compared with other distance functions.

This paper presents an off-line signature verification system composed of a combination of several different classifiers. Identity authentication is a very important characteristics specially in systems that requires a high degree of security such as in bank transactions. In our experiments, one-class classifier was used to create a signature verification system, consequently only genuine signatures were necessary for the training phase. We proposed five distances measurement as features for the classification system. The distances extracted from the signature database were: furthest, nearest, template, central and ncentral. Also, a normalization procedure was applied to turn the distance scale invariant. These distances were combined using four operation: product, mean, maximum and minimum. The calculated distances were used as a feature vector to represent the signatures. Finally, the distances measurement and their combinations were used as input vector for different classifiers. The proposed signature verification method obtained very good rates.

Modular neural networks (MNNs) architectures have been developed aiming to outperform single neural nets. One of the main drawbacks in the construction of the MNNs is the task decomposition which consists in divide the problem into simpler sub-problems. This paper proposes a novel task decomposition method in which the classes of the problem can be divided redundantly. Thus, two different expert modules can have the same class. This is specially interesting for problems that have multimodal classes. The proposed MNN, called Redundant Pattern Distributor, is compared with other ones over many databases and the results show its effectiveness.

This paper presents a novel approach for cursive character recognition by using multiple feature extraction algorithms and a classifier ensemble. Several feature extraction techniques, using different approaches, are extracted and evaluated. Two techniques, Modified Edge Maps and Multi Zoning, are proposed. The former one presents the best overall result. Based on the results, a combination of the feature sets is proposed in order to achieve high recognition performance. This combination is motivated by the observation that the feature sets are both, independent and complementary. The ensemble is performed by combining the outputs generated by the classifier in each feature set separately. Both fixed and trained combination rules are evaluated using the C-Cube database. A trained combination scheme using a MLP network as combiner achieves the best results which is also the best results for the C-Cube database by a good margin.

In this paper it is introduced a new perturbative approach for time series forecasting. The model uses the error of the series, that is the difference between real value of the series and the output of a predictive method, to improve the series forecasting. The methodology proposed is inspired in the Perturbation Theory, that consists in a set of approximation schemes used to describe a complicated problem in terms of simpler ones. For an experimental investigation, this theory, is combined with the TAEF method, that has interesting results when compared with the literature. This combination is called P-TAEF (Perturbative TAEF). Its results over some time series are discussed and compared with previous results found in the literature. It was used several performance measures that showed the robustness of the perturbative approach.

Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information obtained from exogenous series used in combination with neural networks to predict stock series. The best trained neural networks were used in combination to improve the prediction capacity of a single networks. To evaluate the proposed prediction models, some known metrics were applied. Moreover, we also proposed one new metric called Prediction in Direction and Accuracy (PDA), which benefits models with great performance in prediction accuracy and trend. Addictionally, there was used an evolutionary algorithm to choose the best trained models that maximize PDA. Experiments with two of the most important Brazilian companies stock quotes have shown the usefulness of the proposed prediction system to generate profits in investments.

Classical feature extraction techniques, like PCA and LDA, do not deal properly with multimodal problems. Such techniques create projections that do not preserve the multimodal structure of the original data distribution. Locality Preserving Projection (LPP) is a feature extraction technique which looks for a transformation matrix that minimizes the changes into the structure of the data after the transformation. This local structure is captured by the affinity matrix. However, there many ways to calculate this affinity matrix. The main aim of this paper is to evaluate the influence of different affinity matrices over the LPP accuracy. The experiments showed that the correct choice of the affinity matrix can lead to a performance gain. Among the analyzed affinity matrices, Local Scaling and Nearest Neighbor reached the best results.

After the handwritten segmentation process, it is common to have connected digits. This is due to the great size and shape digit variations. In addition, the acquisition and the binarization processes can add noise to the images. These under segmented images, when given as input to classifiers which are specialists to deal with digits separately, should lead to errors. Aiming to detect the handwritten connected digits, it is herein introduced a hybrid system architecture to be used as a segmentation pos-processing task. The proposed system is based on a prototype selection scheme that combines self-generating prototypes and Gaussian mixtures. Besides, this work presents a set of features for the proposed problem. A real-world database of handwritten digits was used to validate the new approach. The results obtained in the experimental study showed that the hybrid strategy achieved promising accuracy rates.

Text extraction is an important phase in document recognition systems. In order to segment text from a page document it is necessary to detect all the possible manuscript text regions. In this article we propose an efficient algorithm to segment handwritten text lines. The text line algorithm uses a morphological operator to obtain the features of the images. Following, a sequence of histogram projection and recovery is proposed to obtain the line segmented region of the text. First, an Y histogram projection is performed which results in the text lines positions. To divide the lines in different regions a threshold is applied. After that, another threshold is used to eliminate false lines. These procedures, however, cause some loss on the text line area. So, a recovery method is proposed to minimize this effect. In order to detect the extreme positions of the text in the horizontal direction, an X histogram projection is applied. Then, as in the Y direction, another threshold is used to eliminate false words. Finally, in order to optimize the area of the manuscript text line, a text selection is carried out. Experimental results using the IAM-database showed that this new approach is robust, fast and produces very good score rates.

The present work utilizes the framework Clonart (Clonal Adaptive Resonance Theory) that employs many different techniques such as intelligent operators, clonal selection principle, local search, memory antibodies and ART clusterization in order to increase the performance of the algorithm. The approach uses a mechanism similar to the ART 1 network for storing a population of memory antibodies that will be responsible for the acquired knowledge of the algorithm. This characteristic allows the algorithm a self-organization of the antibodies in accordance with the complexity of the database used. A face recognition test case was applied to estimate the performance of this framework with different problem domains.

Task decomposition is a widely used method to solve complex and large problems. In this paper, it is proposed a novel task decomposition approach, named tree architecture pattern distributor (TreeArchPD), which is based on another task decomposition technique, called pattern distributor. The main idea is to design a tree architecture with many distributors instead of using only one distributor as proposed by the original technique. It is also proposed a new class grouping method that aims to optimize the class selection for task decomposition. Many experiments were done and they showed the effectiveness of the proposed approaches.

This paper presents a new neural network to perform the visual pattern classification task. The neural network is calledI-PyraNetwhich is a hybrid implementation of the PyraNet and the concepts of the inhibitory fields. In order to improve the results obtained by this neural network, it is also presented the 2-D Gabor filter. Furthermore, both, the neural network and the filter, are applied over a face detection task and are compared to the results obtained by a SVM.

Time series forecasting have been a subject of interest in several different areas of research such as: meteorology, demography, health, computer and finance. Since it can be applied to various practical problems in real world, techniques to predict time series have been a topic of increasing research activities, especially in the financial sector that has a great interest in the forecast of the stock market. In this article, we are interested in the forecast of the time series related to the Brazilian oil company, Petrobras (PETR4). A methodology based on information obtained from exogenous series was used in combination with a neural network to predict the PETR4 stock series. Exogenous series were selected by analyzing the correlation between the series with the Petrobras stocks series. In this way, the prediction was obtained by not just using the previous values of the series but also by using information external to the PETR4 series. The values of the selected series were used as features for a prediction stage based on combined neural networks. To evaluate the performance of the system classical measurements were used, however we also introduce a new performance index called Sum of the Losses and Gains (SLG).

One of the most successful process to accomplish human face recognition are the methods based on the principal component analysis (PCA), also known as eigenfaces. Recently, novel PCA approaches have been proposed: modular (MPCA) and two-dimensional (IMPCA). These approaches have achieved outstanding result in feature extraction and recognition. IMPCA is used for feature extraction based on 2D matrix representation and MPCA is based on image division to improve face recognition with variations like facial expressions, light and head pose. In this work we use some aspects of these methods to build a new technique called modular Image PCA (MIMPCA). The results achieved with the proposed method are superior in all experiments compared with the original techniques under different conditions of head pose angle, illumination and facial expression.

This paper presents a new model to realize a supervised image segmentation task. It is based on the concept of receptive fields that intends to analyze pieces of an image considering not only the pixels or group of them, but also the relationship between them and their neighbors, called segmentation and classification with receptive fields (SCRF). Also, in order to work with the SCRF model, is proposed here a new artificial neural network, called IPyraNet, which is a hybrid implementation of the recently described PyraNet and the nonclassical receptive fields inhibition. Furthermore, the model and the network are applied together in order to realize a satellite image segmentation task.

Machines learning techniques have been applied in several different problems in bioinformatics. Similarly, pattern discovery algorithms have also been used to uncover hidden motifs in protein sequences, contributing greatly to the understanding of the problem of protein classification. G-protein coupled receptors (GPCRs) represent one of the largest protein families in Human Genome. Most of these receptors are major target for drug discovery and development. Therefore, they are of interest to the pharmaceutical industry. The technique used in this paper combine machine learning and pattern discovery methods to develop a protein prediction procedure in relation to its functional class, more specifically to predict GPCR protein class. Vilo[2]proposed an algorithm in order to extract pattern of regular expressions from known protein GPCR sequences and used them to predict coupling specificity of G protein coupled receptors to their G proteins. We analyze these patterns and combine them as features for feeding a SVM to predict the GPCR super class. We demonstrate the results using ROC curves, which are well-indicated to evaluate the performance of this kind of classifiers. The experiments, based on the GPCRDB database, also showed that we were able to find some novel GPCR sequences that were not described in the PROSITE database.

This paper presents a new artificial neural network, called I-PyraNet. This new architecture is based on the combination between concepts of the recently described PyraNet and the nonclassical receptive fields inhibition, integrating the feature extraction and the classification stages into the same structure which is formed by 2-D and 1-D layers. The main difference between the PyraNet and the I-PyraNet is that while in the first a 2-D neuron always provide the same output, in the I-PyraNet the signal of the output of a 2-D neuron will invert when it appears inside a inhibitory field. Furthermore, the I-PyraNet is applied over a face detection task where different configurations of the network are tested.

This work focuses on a study about hybrid optimization techniques for improving feature selection and weighting applications. For this purpose, two global optimization methods were used: Tabu search (TS) and simulated annealing (SA). These methods were combined to k-nearest neighbor (k-NN) composing two hybrid approaches: SA/k-NN and TS/k-NN. Those approaches try to use the main advantage from the global optimization methods: they work efficiently in searching for solutions in the global space. In this study, the methodology is proposed by [4]. In the referred work, a hybrid TS/k-NN approach was suggested and successfully applied for feature selection and weighting problems. Based on the later, this analysis indicates a new SA/k-NN combination and compares their results using the classical Euclidean Distance and a Simple Adaptive Distance [8]. The results demonstrate that feature sets optimized by the studied models are very efficient when compared to the well-known k-NN. Both accuracy classification and number of features in the resultant set are considered in the conclusions. Furthermore, the combined use of the simple adaptive distance improves even more the results for all datasets analyzed.

In this paper, we present an artificial neural network (ANN) architecture for segmenting unconstrained handwritten sentences in the English language into single words. Feature extraction is performed on a line of text to feed an ANN that classifies each column image as belonging to a word or gap between words. Thus, a sequence of columns of the same class represents words and inter-word gaps. Through experimentation, which was performed using the IAM database, it was determined that the proposed approach achieved better results than the traditional Gap Metric approach for handwriting sentence segmentation.

This paper presents an investigation into prototype-based classifiers. Different methods have been proposed to deal with this problem. There are two main classes of prototype-selection algorithms. The first is merely selective, in which the resulting set of prototypes is formed by well-chosen samples from the training set. The second is known as the creative class of algorithms. This strategy creates new instances and performs adjustments of the prototypes during training. Two methods of the creative strategy are presented here: a self-generating prototype scheme and a fuzzy variation of Nearest Prototype Classification, which uses a Gaussian Mixture ansatz. The respective advantages and problems are discussed. A hybrid method is proposed to overcome difficulties and improve accuracy. The hybrid strategy obtained better results in the experiments when compared to each of two basic approaches and the classic K-Nearest Neighbor.

Techniques based on biometrics have been successfully applied to personal identification systems. One rather promising technique uses the keystroke dynamics of each user in order to recognize him/her. In the present study, we present the development of a hybrid system based on support vector machines and stochastic optimization techniques. The main objective is the analysis of these optimization algorithms for feature selection. We evaluate two optimization techniques for this task. genetic algorithms (GA) and particle swarm optimization (PSO). We use the standard GA and we created a PSO variation, where each particle is represented by a vector of probabilities that indicate the possibility of selecting a particular feature and directly affects the original values of the features. In the present study, PSO outperformed GA with regard to classification error, processing time and feature reduction rate.

This paper discusses the problem of size variation in on-line signature verification systems. The main idea of the article is to investigate the influence of the size variation in the feature extraction techniques and how this distortion can affect the final classification performance of the systems. In this study a new classification approach was suggested based on Kholmatov and Yanikoglu work in order to measure this performance. Besides that, a feature selection technique was applied in the description of the patterns with the purpose of over come the size variation problem. All the experiments were performed in a database constructed with signatures of three different sizes and skilled forgeries. This kind of study plays an important role in the implementation of systems that uses different signature sources.

Techniques based on biometrics have been successfully applied to personal identification systems. One rather promising technique uses the keystroke dynamics of each user in order to recognize him/her. In this work, we present the development of a hybrid system based on support vector machines and stochastic optimization techniques. The main objective is the analysis of these optimization algorithms for feature selection. We evaluate two optimization techniques for this task: genetic algorithms (GA) and particle swarm optimization (PSO). In the present study, PSO outperformed GA with regard to classification error and processing time, but was inferior regarding the feature reduction rate.

The correct segmentation and measurement of mammography images is of fundamental importance for the development of automatic or computer-aided cancer detection systems. In this paper we propose a method to segment mammogram image using a self-organizing neural network based on spatial isomorphism. The method used is a modified version of the algorithm proposed by Venkatesh and Rishikesh [1] to extract object boundaries in an image. This model explores the principle of spatial isomorphism and self-organization in order to create flexible contours that characterize shapes in images. We modified the original algorithm to overcame problems of local minimum, poor performance for image object with large concavity and imprecise results when simple or far from object border contour are chosen. A comparison of both algorithm and original segmentation used by the MIAS database [9] is presented.

This paper proposes a new method for binarization of digital documents. The proposed approach performs binarization by using a heuristic algorithm with two different thresholds and the combination of the thresholded images. The method is suitable for binarization of complex background document images. In experiments, it obtained better results than classical techniques in the binarization of real bank checks.

This paper deals with automatic recognition of real bank checks. A new approach is proposed to read the numerical amount field from bank checks, considering the numeric value and the different delimiters that might exist in that field. The proposal combines different neural networks classifiers to perform the recognition. Experimental results have shown that this approach is robust and efficient for automatic recognition of real Brazilian bank checks.

The prediction of secondary structure is treated with a simple and efficient method. Combining only three neural networks, an average Q/sub 3/ accuracy prediction by residues of 75.93% is achieved. This value is better than the best results reported on the same test and training database, CB396, using the same validation method. For a second database, RS126, an average Q/sub 3/ accuracy of 74.13% is attained, which is better than each individual method, being defeated only by CONSENSUS, a rather intricate engine, which is a combination of several methods. The networks are trained with RPROP an efficient variation of the back-propagation algorithm. Five combination rules are applied independently afterwards. Each one increases the accuracy of prediction by at least 1%, due to the fact that each network used converges to a different local minimum. The Product rule derives the best results. The predictor described here can be accessed at http://biolab.cin.ufpe.br/tools/.

Face recognition is an important area of research with many applications, including biometric security and searching face databases. This article describes an approach to recognize faces using eigenbands, which aim to capture the best features from facial characteristics. Faces are divided in vertical and horizontal bands. From each band is extracted features using standard-PCA. Results show that the standard-PCA applied over vertical band faces, using Bayes classifier, was more accurate than previous methods reported on the ORL face database.

The PCA linear transformation method is used for feature extraction to the secondary structure prediction problem. The method of dimensionality reduction is applied on PSI-Blast profiles built on NCBI's Nonredundant Protein database. Different numbers of components extracted are used as input to three artificial neural networks with 30, 35 or 40 nodes in the hidden layer. Those classifiers are trained with the RPROP algorithm. To estimate the accuracy of the predictor the sevenfold cross-validation method is applied to CB396, a database used previously to evaluate the performance of several predictors. Aiming to increase the efficiency of the predictor presented here, the outputs of the classifiers are combined through five simple rules: product, average, voting, minimum and maximum. This original application for the PCA method derives relevant results. Even with a drastic reduction from 260 to 80 components, the accuracy obtained is at least 1% superior to the best one published for another predictor, the CONSENSUS, a combination of four other predictors. With a reduction from 260 to 180 components the performance is even better, achieving an Q/sub 3/ accuracy of 74.5%. The results flag the PCA as a promising method for feature extraction in the secondary structure prediction problem.

The main goal of this work is to investigate a computational architecture for a document management environment. Its purpose is to digitalize and extract information from documents of any type, transforming them into structured electronic documents. The environment is divided into specification and extraction modules. In the first module, the user performs the document specification, capturing physical, semantic and process information from the document. The extraction module uses this information, in order to recognize documents of the same class as the specified one. This environment offers the possibility of visualizing the classification results and to correct eventual mistakes it has made. Also, it allows document reconstruction from physical and semantic information captured in the specification module.