In the framework of Alzheimer’s disease prediction systems, it is widely agreed that handwriting seems to be one of the first skills to be influenced by the onset of such a disease. In the large majority of cases, the above systems consider information relating to the dynamics of the handwriting process, directly derived from online handwriting samples. This kind of features, however, are not able to capture the alterations in the shape, size and thickness of the handwritten traits, which may be produced by the alterations in motor control due to neurodegenerative disorders. Following this line of thought, in a previous study we combined shape and dynamic information by generating synthetic color images from online handwriting samples, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated to that trait. Finally, we exploited the capability of Deep Neural Networks to automatically extract features from raw images, following the Transfer Learning approach. The results obtained with this approach did not show significant improvements compared to those obtained with the use of dynamic information only, probably because approximating the original traits with straight lines of predefined thickness results in a loss of information on their actual shape and thickness. Moving from these considerations, the purpose of our study is to verify whether automatically extracting features directly from offline handwriting images, thus considering the original shape of the handwritten trace, could provide better results. Again, we exploited the capability of Deep Neural Networks to automatically extract features from raw images. The preliminary experimental results confirmed the effectiveness of the proposed approach.

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of AD. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. To verify the role played by dynamic information, we also generated simple binary images, containing only shape information. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. The experimental results have confirmed that dynamic information allows a performance improvement with respect to the binary images.

Early detection of Alzheimer's Disease (AD) is essential in order to initiate therapies that can reduce the effects of such a disease, improving both life quality and life expectancy of patients. Among all the activities carried out in our daily life, handwriting seems one of the first to be influenced by the arise of neurodegenerative diseases. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in this research field in order to make a diagnosis as early as possible. In recent years, many studies have tried to use classification algorithms applied to handwriting to implement decision support systems for AD diagnosis. A key issue for the use of these techniques is the detection of effective features, that allow the system to distinguish the natural handwriting alterations due to age, from those caused by neurodegenerative disorders. In this context, many interesting results have been published in the literature in which the features have been typically selected by hand, generally considering the dynamics of the handwriting process in order to detect motor disorders closely related to AD. Features directly derived from handwriting generation models can be also very helpful for AD diagnosis. It should be remarked, however, that the above features do not consider changes in the shape of handwritten traces, which may occur as a consequence of neurodegenerative diseases, as well as the correlation among shape alterations and changes in the dynamics of the handwriting process. Moving from these considerations, the aim of this study is to verify if the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them a synthetic off-line colour image, where the colour of each elementary trait encodes, in the three RGB channels, the dynamic information associated to that trait. Finally, we exploited the capability of Deep Neural Networks (DNN) to automatically extract features from raw images, following the Transfer Learning approach. The experimental comparison of the results obtained by using standard features and features extracted according the above procedure, confirmed the effectiveness of our approach.

Bakurov, I., Castelli, M., Fontanella, F., & Vanneschi, L. (2019). A regression-like classification system for geometric semantic genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) (Vol. 1, pp. 40-48). (IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence). SciTePress.

Structure learning is a very important problem in the context of Bayesian networks (BNs). For this reason, it has been largely studied in the last few years and many approaches have been presented to find an optimal structure based on training samples. In a previous paper, we proposed an evolutionary algorithm for BN structure learning based on a data structure specifically devised for encoding BNs. The proposed approach was used to combine the responses of classifier ensembles. In this paper, we present a further improvement along this direction, in that we have developed a novel mutation operator that allows a more effective exploration of the search space. The devised operator is able to modify both node ordering and the connection topology of the encoded BN. The experimental results, obtained by using five benchmark datasets, confirmed the effectiveness of the proposed approach.

The form processing systems commercially available include a verification step during which a human operator verifies the output provided by the system to ensure 100% accuracy. In order to reduce the time and the cost of such a stage, the OCR engine incorporated into the system provides a reliability measure of the classification to be used for implementing a reject option: in this way only rejected samples are passed to the verification stage. Most of the strategies for designing such a reject option consider that the source of classification errors are within the OCR engine. Such an assumption becomes less reasonable as the forms become less structured, as in case when boxes are provided for the entire data field and not only for isolated characters. Under these circumstances, we investigate to which extent the reliability measure provided by an OCR engine designed to deal with boxed isolated characters can be used to detect both segmentation and classification errors. The experimental results, obtained on a large data set of forms currently in use by a large organization, show that the proposed method successfully achieves its aim. It represents a powerful tool for the system manager to plan system enhancement as the volume of forms containing less constrained data fields increases.

In many real world applications, the cost of a wrong decision may be much higher than the benefit of having a high classification rate. This kind of classification problems represent very challenging tasks, because they require highly reliable systems and may benefit of introducing a reject option. To this purpose, many classification systems have been proposed, among which classifier ensembles represent a successful example. This approach aims at combining classifiers making uncorrelated errors. In this framework, the Random Forest (RF) represents a case of special interest. A RF is made of a suitable ensemble of decision trees and has proved to be very effective in several fields. In this paper, the RF classification reliability is experimentally analyzed with reference to the case of handwriting recognition. The aim is to verify if such reliability can be effectively used to introduce a new reject option, which considers, for each unknown sample, a subset of few classes including with high probability the correct one. The whole system operates as a pre-classification stage and the reject option should allow us to obtain a low error rate without significantly affecting the recognition rate. Experiments, carried out on two real world datasets, have shown the effectiveness of the proposed method.

Classifier combination methods have proved to be an effective tool for increasing the performance in pattern recognition applications. The rationale of this approach follows from the observation that appropriately diverse classifiers make uncorrelated errors. Unfortunately, this theoretical assumption is not easy to satisfy in practical cases, thus reducing the performance obtainable with any combination strategy. In this paper we propose a new weighted majority vote rule which try to solve this problem by jointly analyzing the responses provided by all the experts, in order to capture their collective behavior when classifying a sample. Our rule associates a weight to each class rather than to each expert and computes such weights by estimating the joint probability distribution of each class with the set of responses provided by all the experts in the combining pool. The probability distribution has been computed by using the \textit{naive Bayes} probabilistic model. Despite its simplicity, this model has been successfully used in many practical applications, often competing with much more sophisticated techniques. The experimental results, performed by using three standard databases of handwritten digits, confirmed the effectiveness of the proposed method.

We propose a feature selection--based approach for improving classification performance of a two stage classification system in contexts where a high number of features is involved. A problem with a set of $N$ classes is subdivided into a set of $N$ two class problems. In each problem, a GA--based feature selection algorithm is used for finding the best subset of features. These subsets are then used for training $N$ classifiers. In the classification phase, unknown samples are given in input to each of the trained classifiers by using the corresponding subspace. In case of conflicting responses, the sample is sent to a suitably trained supplementary classifier. The proposed approach has been tested on a real world dataset containing hyper--spectral image data. The results favourably compare with those obtained by other methods on the same data.

Combining classifier methods have shown their effectiveness in a number of applications. Nonetheless, using simultaneously multiple classifiers may result in some cases in a reduction of the overall performance, since the responses provided by some of the experts may generate consensus on a wrong decision even if other experts provided the correct one. To reduce these undesired effects, in a previous study, we proposed a combining method based on the use of a Bayesian Network. In this paper, we present an improvement of that method which allows to solve some of the drawbacks exhibited by standard learning algorithms for Bayesian Networks. The proposed method is based on an Evolutionary Algorithm which uses a specifically devised data structure to encode direct acyclic graphs. This data structure allows to effectively implement crossover and mutation operators. The experimental results, obtained by using three standard databases, confirmed the effectiveness of the method.

We present a Genetic Algorithm based feature selection approach according to which feature subsets are represented by individuals of an evolving population. Evolution is controlled by a fitness function taking into account statistical properties of the input data in the subspace represented by each individual, and aims to select the smallest feature subset that optimizes class separability. The originality of our method lies particularly in the definition of the evaluation function. The proposed approach has been tested on a standard database of handwritten digits, showing to be effective both for reducing the number of features used and for improving classifier performance.

We propose a new feature selection algorithm for remote sensing image classification. Our approach has been especially devised for applications in which there is a large number of different features that can be potentially selected, implying that the search space is complex and high-dimensional. In this framework, our proposal is that of reformulating the feature selection problem as the search for the optimal subspace in which the different classes are more effectively discriminated. The search has been performed by using a genetic algorithm in which each individual encode the choice of a subspace, and its fitness is a measure of the class seperability in that subspace. The experimental results, performed on two databases, confirmed the effectiveness of the approach.

A new prototyping method based on the evolutionary computation paradigm and on the concept of Vector Quantization is proposed. It uses a specifically devised evolutionary algorithm for evolving a set of prototype feature vectors and does not require any a priori knowledge about either the actual number of prototypes or the statistical properties of the input data. Experiments performed by using both synthetic data and handwritten digits randomly extracted from the NIST database have confirmed the effectiveness of the approach.

We propose a genetic programming based approach for generating prototypes in a classification problem. In this context, the set of prototypes to which the samples of a data set can be traced back is coded by a multitree, i.e. a set of trees, which represents the chromosome. Differently from other approaches, our chromosomes are of variable length. This allows coping with those classification problems in which one or more classes consist of subclasses. The devised approach has been tested on several problems and the results compared with those obtained by a different genetic programming based approach recently proposed in the literature.