Drone-view object detection (DroneDet) is a computer-vision method for locating objects and predicting their categories in aerial images. Popular methods are often plagued by high energy consumption and severe noise interference in dynamic aerial environments. In this study, we propose a novel integer-valued spiking neural network based approach called SNN-Drone to optimize both energy consumption and performance. Regarding high energy consumption, a new Integer-Valued Encoding (IVE) scheme transforms YOLOv8 features into high-fidelity integer spike trains. The IVE effectively reduces the high energy consumption associated with conventional binary spike encoding. For noise interference, our Dual-Branch Spike Block (DBSB) enhances feature representation by separating spatial and semantic processing, while the Normalized Wasserstein Distance (NWD) loss refines localization accuracy for small and densely packed targets. These modules significantly improve detection performance. In addition, we construct UAVDT-SNN, the first large-scale neuromorphic dataset for UAV object detection with temporally encoded motion and diverse aerial scenes. Extensive experiments on the VisDrone 2019, UAVDT, and UAVDT-SNN datasets demonstrate that SNN-Drone achieves state-of-the-art performance, outperforming both baseline ANNs and advanced YOLOv8–v12 variants. Notably, SNN-Drone-s improves energy efficiency by 7.7× compared to YOLOv8s. This work is the first to show that SNNs can serve as an energy-efficient yet competitive solution for drone-view object detection, narrowing the performance–energy gap with ANNs. Our code is available at: https://github.com/iliaohai/SNN-Drone.

Active learning (AL) is a semi-supervised learning paradigm with human-machine interaction and a limited annotation budget. However, few AL studies have explored distribution inconsistency between the data and the population. In this paper, we consider a basic form of the aforementioned issue,i.e., the training data is non-independently and identically distributed (non-IID) sampled from a class uniformly distributed population. Accordingly, we propose a naïve sample selection plugin, namely generalized active stratified sampling (GASS), to rebalance the sample size of each class during AL iterative process, resulting in a progressive approximation to the population. We generalize statistical stratified sampling to support the uncertainty strata criterion, forming the statistical foundation of GASS. This method, as a plugin, can seamlessly collaborate with popular information-based strategies. GASS shows superior rebalancing capabilities by analyzing the statistical moment and the class imbalanced index under the Probably Approximately Correct (PAC) theory. Furthermore, models derived with GASS have low Rademacher complexity (RC), indicating low generalization error bounds, and GASS also exhibits strong robustness to prediction perturbations. Experiments were conducted on 5 benchmark image datasets, and the results show that GASS significantly boosts the test accuracy by about$2.38\%$/$3.19\%$(paired$t$-test$p=0.01$/0.04) and reduces the empirical RC by about$1.42\%$/$1.94\%$(paired$t$-test$p=0.01$/0.05) on average in class imbalanced/balanced scenarios, respectively. This study establishes a potential benchmark for information-based AL.

Outliers are intrinsic to recommender systems (RSs) due to user uncertainty and bring large deviations to the total loss. Classical loss functions such as$L_{1}$and$L_{2}$do not consider this issue, thus biasing the model toward abnormal ratings. Advanced ones such as Logcosh, Huber, and Wing losses try to control model biasing; however, they do not consider the rating ranges of recommender systems. In this article, we propose a Seagull loss combining quadratic and softsign functions to handle this issue. The quadratic function aims to preserve or appropriately increase losses caused by small deviations, while softsign function aims to suppress losses caused by large deviations. It is distinguished from three types of popular losses. Compared with$L_{1}$and$L_{2}$, it controls the upper bound, therefore suppressing the influence of outliers. Compared with Logcosh and Huber, it not only adjusts the loss of small deviations but also suppresses the loss of large deviations. Compared with Wing loss, it considers the characteristics of data in RSs and therefore is more suitable to the matrix factorization model. Experiments are undertaken on six real-world recommendation datasets in comparison with six popular loss functions. Results show that Seagull loss function has superiority to all losses above on mean absolute error (MAE) and root mean square error (RMSE) and slight inferiority to$L_{2}$and Wing on hit ratio (HR), mean average precision (MAP), and normalized discounted cumulative gain (NDCG).

Adversarial examples have posed a serious threat to deep neural networks due to their transferability. Existing transfer-based attacks tend to improve the transferability of adversarial examples by destroying intrinsic features. However, prior work typically employed single-dimensional or additive importance estimates, which provide inaccurate representations of features. In this work, we propose the Multi-Feature Attention Attack (MFAA), which fuses multiple layers of feature representations to disrupt category-related features and thus improve the transferability of the adversarial examples. First, MFAA introduces a layer-aggregation gradient (LAG) to obtain guidance maps, which reflect the importance of features in multiple scales. Second, it generates ensemble attention (EA), preserving object-specific features and offsetting model-specific features based on the guidance maps. Third, EA is iteratively disturbed to achieve high transferability of the adversarial examples. Empirical evaluation on the standard ImageNet dataset shows that adversarial examples crafted by MFAA can effectively attack different networks. Compared to the state-of-the-art transferable attacks, our attack improves the average attack success rate of the black-box model with defense from 88.5% to 94.1% on single-model attacks and from 86.6% to 95.1% on ensemble attacks. Our code is available at Github: https://github.com/KWPCCC/MFAA.

Deep active learning (DeepAL) extends supervised deep learning to human-machine interactive scenarios with limited annotation budgets. Most existing DeepAL approaches for visual recognition fail to consider the intrinsic hierarchical structure and dependencies between labels. In this article, we propose a unified DeepAL framework for the aforementioned challenge, which fuses three tightly coupled techniques: 1) hierarchical dependency representation entropy (HDRE); 2) approximate class-balanced typical sampling (ACTS); and 3) local probability suppression loss. First, the HDRE provides the features of information entropy, interclass dependencies, and constraints effectively. It is used to determine the query priority of unlabeled samples. Second, the ACTS, embedded with the HDRE, is designed for querying, where the optimal sample query size of each class is derived. It excludes samples near the boundary by employing a well-designed hierarchical margin sampling. Third, the local probability suppression loss is a transfer-friendly loss function that enables the deep model to flatly fit data with a hierarchical structure. It compensates for hierarchical dependencies between classes using the local probability suppression constraint, modeling conditional and unconditional probabilities simultaneously. We conducted experiments on five public image datasets, and the results demonstrated the effectiveness of our approach.

In single-well production prediction tasks, the achieved forecasting performance is sensitive to various uncertain factors. Most existing methods are either based on trend lines of the production history or overall development process data. However, the linkages between diverse data types and models have largely not been considered. In this paper, we construct a novel stacking model to fuse the static geological information, dynamic well production history, and spatial information of adjacent water injection wells. This stacking model consists of three basic modules that serve as encoders to separately extract features from these three types of information. The first is a multilayer perceptron (MLP), which analyzes the static geological properties of the reservoir that might influence the well production rate. The second is a long short-term memory (LSTM) module, which extracts the temporal information that is reflected in dynamic production variations. The third is also an LSTM module, as a spatial information processing module , it considers the time delay of the water flooding response from the injection well to the target well. The information fusion quality of the stacking model is validated from the perspective of causality discovery using symbolic transfer entropy (STE). Experiments are conducted on a block dataset derived from a water flooding oilfield. The results show that the presented model can make full use of its model structure and greatly improve the accuracy and generalizability of prediction.

Effective identification of anomalous data from production time series in the oilfield affects future analysis and forecasting. Such time series is often characterized by irregular time intervals due to uneven manual sampling, and missing values caused by incomplete measurements. Therefore, the identification task becomes more challenging. In this paper, an Attention-Embedded Time-Aware Imputation Network (ATIN) with two sub-networks is proposed for this task. First, Time-Aware Imputation LSTM (TI-LSTM) is designed for modeling irregular time intervals and incomplete measurements. It decays the long-term memory component as the producing well conditions may be varied during the water cut stage. Second, Attention-Embedding LSTM (ATEM) is designed to improve the effectiveness of anomaly detection. It focuses on the correlation between the last and historical measurements in a given sequence. Comparison experiments with several state-of-the-art methods, including mTAN, GRU-D, T-LSTM, ATTAIN, and BRITS are conducted. Results show that the proposed ATIN performs better in accuracy, F1-score, and area under curve (AUC).

Deep learning full waveform inversion (DL-FWI) is an end-to-end and time-efficient high-resolution imaging technique for subsurface media. Popular methods are often plagued by location drift and significant velocity misfits at the stratigraphic boundaries. In this study, we propose an augmented boundary attention algorithm (ABA-FWI) to focus on the key boundary information. Regarding network composition, the wavelet convolution (WTconv) layer and the spatial attention module (SAM) are incorporated into the encoder and the decoder, respectively. The WTconv layer captures low frequencies by obtaining a large receptive field without suffering from overparameterization. SAM extracts distinctive information by utilizing the interspatial relationship of features for resolution enhancement. For loss function design, our reflection coefficient tuned boundary (RCTB) loss introduces the reflection coefficient to adjust the gradient map weight of low-contrast areas. It focuses on boundary regions characterized by speed transitions to minimize errors. Results on OpenFWI, the SEG simulation, and the Marmousi II slice datasets show that our method is superior to the state-of-the-art data-driven methods, especially on boundary details. The source code is available athttps://github.com/FanSmale/ABA-FWI.

Deep learning full waveform inversion (DL-FWI) is gaining much research interest due to its high prediction efficiency, effective exploitation of spatial correlation, and lack of the need for an initial estimate. As a data-driven approach, it has several key issues. For example, effective deep networks need to be designed, the training process needs to be controlled, and the generalization ability needs to be enhanced. In this paper, we propose a dual decoder network with curriculum learning (DD-Net) to handle these issues. First, we design a U-Net with two decoders to grasp the velocity value and stratigraphic boundary information of the velocity model. These decoders’ feedback will be combined at the encoder to enhance the encoding of edge spatial information. Second, we introduce curriculum learning to network training by organizing data in three difficulty levels. The easy-to-hard training process enhances the data fitting of the network. Third, we apply the network to low resolution seismic observations via a pre-network dimension reducer. This can serve as a general design idea without destroying the original network characteristics. Experiments are undertaken on SEG salt datasets and four synthetic datasets from OpenFWI. The results show that our network is superior to other state-of-the-art data-driven networks. The source code is available at github.com/fansmale/ddnet.

Label distribution learning (LDL) is an effective learning paradigm for dealing with label ambiguity. When applying LDL, the datasets annotated with label distributions (i.e., the real-valued vectors like the probability distribution) are typically required. Unfortunately, most existing datasets only contain the logical labels, and manual annotating with label distributions is costly. To address this problem, we treat the label distribution as a latent vector and infer its posterior by variational Bayes. Specifically, we propose a generative label enhancement model to encode the process of generating feature vectors and logical label vectors from label distributions in a principled way. In terms of features, we assume that the feature vector is generated by a Gaussian mixture dominated by the label distribution, which captures the one-to-many relationship from the label distribution to the feature vector and thus reduces the feature generation error. In terms of logical labels, we design a probability distribution to generate the logical label vector from a label distribution, which captures partial label ranking in the logical label vector and thus provides a more accurate guidance for inferring the label distribution. Besides, to approximate the posterior of the label distribution, we design a inference model, and derive the variational learning objective. Finally, extensive experiments on real-world datasets validate our proposal.

Label distribution learning (LDL) employs probabilistic labels to capture the varying degrees of relevance among decision attributes. Existing LDL algorithms usually employ sample correlations to improve their predictive validity. However, they merely employ the superficial features of the sample for correlation analysis, seldom delving into its latent deep features. In this paper, we propose an algorithm to explore the sample topology (ST-LDL) to address this problem. First, we construct a locally weighted directed graph for each target sample. The target sample and its k neighbors are regarded as nodes within the graph. The asymmetrical correlation among these $k+1$ samples is computed separately to determine the weight value of the graph. Then, we utilise the local topology between samples to mine latent information and reconstruct the samples. The local graph is fed into the graph convolutional network to nonlinearly reconstruct the features by mining the latent information. Finally, we designed a new optimization objective function for the reconstructed samples. Experiments are carried out on elven real-world datasets in comparison with seven state-of-the-art algorithms. The results show that our algorithm outperforms several other algorithms, proving the effectiveness of our algorithm.

Co-training is a popular semi-supervised learning method. The learners exchange pseudo-labels obtained from different views to reduce the accumulation of errors. One of the key issues is how to ensure the quality of pseudo-labels. However, the pseudo-labels obtained during the co-training process may be inaccurate. In this paper, we propose a safe co-training (SaCo) algorithm for regression with two new characteristics. First, the safe labeling technique obtains pseudo-labels that are certified by both views to ensure their reliability. It differs from popular techniques of using two views to assign pseudo-labels to each other. Second, the label dynamic adjustment strategy updates the previous pseudo-labels to keep them up-to-date. These pseudo-labels are predicted using the augmented training data. Experiments are conducted on twelve datasets commonly used for regression testing. Results show that SaCo is superior to other co-training style regression algorithms and state-of-the-art semi-supervised regression algorithms.

Seismic analysis and interpretation are sensitive to data resolution. High-density acquisition, deconvolution, and inverse Q filtering are traditional methods for resolution improvement. These methods have drawbacks such as strict assumptions and complex parameter solving processes. In this paper, we propose a channel attention U-Net and physical convolution combination (CAUC) algorithm to overcome these limitations. First, we design a channel attention U-Net (CAU) to establish a sophisticated nonlinear relationship between low-frequency data and coarse reflection coefficients. Specifically, we use a channel attention block after each downsampling convolution block to extract important local features of seismic data. Second, we convolve the coarse reflection coefficients with the wavelets to obtain high-resolution data. This physical convolution operation has a solid theoretical foundation. Therefore, our algorithm benefits not only from the learning ability of the neural network but also from the explainability of the physical operation. Both synthetic and field data are employed to verify the validity of the new algorithm. The traditional fast iterative shrinkage-thresholding algorithm (FISTA) and deep convolutional neural network (CNN) are used for comparison. The results show the superiority of the proposed method, especially in enhancing details such as thin layers and continuity.

Deep learning has made substantial progress in remote sensing image segmentation tasks. It usually requires a large number of high-quality annotation maps (i.e., clean labels), which are labor-intensive. In this paper, we propose to use abundant electronic maps (i.e., noisy labels) to supplement a small number of clean labels to solve the problem of massive label production. In addition, we propose a multi-stage noise supervised framework (NSDI) to prevent noise from deteriorating the performance of deep model. NSDI consists ofclean training,weight initialization, andhybrid trainingstages. In theclean trainingstage, we train a segmentation model using a small amount of clean labels and remote sensing images to compute the confusion probability matrix. In theweight initializationstage, we use the confusion probability matrix as well as the prediction probability to calculate the label reliability of the electronic map. In thehybrid trainingstage, an adaptive weighted loss function based on cross-entropy is used to dynamically update the label reliability. Then we train model further using electronic maps with the support of the adaptive weighted loss function and label reliability. Experiments were undertaken on 2,656 images of 512 × 512 pixels. Ablation studies show that NSDI improves the model robustness as well as the segmentation quality.

Fine-grained visual categorization is a challenging issue owing to high intra-class and low inter-class variances. Classical approaches rely on pre-trained models or many fine annotations. In this paper, we observe that spatial and frequency information provides distinct image views, and propose a new spatial–frequency feature fusion (SFFF) perspective to handle this challenging issue. Specifically, we design a heterogeneous feature extraction loss function, construct a global and local fusion SFFF network, and propose an importance-sparsity selection strategy. For feature extraction, we focus on the frequency domain feature learning network, extract fine-grained features, and achieve feature complementarity. For feature selection, we propose importance ranking and sparse regularity to constrain spatial–frequency features. For feature fusion, we design a spatial–frequency loss and an inter-layer switching strategy to achieve local-global collaboration. Comparative experiments were performed on popular fine-grained datasets and classic datasets such as CUB200-2011, Stanford Cars, Stanford Dogs, FGVC-Aircraft, and CIFAR100. The effectiveness and outstanding performance of SFFF are confirmed by comparisons with more than 40 state-of-the-art fine-grained categorization methods. Ablation studies and visualizations are provided to facilitate an understanding of our approach.

Super-resolution reconstruction is an essential task of seismic inversion due to the low resolution and strong noise of field data. Popular deep networks derived from U-Net lack the ability to recover detailed edge features and weak signals. In this paper, we propose a dual decoder U-Net (D2UNet) to explore both detail and edge information of the data. The encoder inputs the low resolution image and the edge image obtained through the Canny algorithm. Edge image can provide rich shape and boundary information, which is helpful to generate more accurate and high-quality data. The dual decoder consists of a main decoder for high-resolution recovery and an edge decoder for edge contour detection. These two decoders interact with a texture warping module (TWM) with deformable convolution. TWM aims to distort realistic edge details to match the fidelity of low resolution inputs, especially the location of edges and weak signals. The loss function is a combination ofL1loss and multi-scale structural similarity loss (MS-SSIM) to ensure perception quality. Results on synthetic and field seismic images show that D2UNet not only improves the resolution of noisy seismic images, but also maintains the image fidelity.

Practical data often suffer from missing attribute values and lack of class labels. A reasonable machine learning scenario involves obtaining certain values and labels at cost on request. In this article, we propose the cost-sensitive active learning through unified evaluation and dynamic selection (CALS) algorithm to handle the learning task in this new scenario. For data representation, we consider misclassification cost, label query cost, and attribute query cost. For the cost/benefit estimation, we design a unified assessment of attribute values and labels with softmax regression. For the selection of attribute value and label, we propose an optimal acquisition scheme with permutation and greedy strategies. We perform experiments with synthetic, benchmark, and domain datasets. The results of the significance test verify the effectiveness of CALS and its superiority over cost-sensitive active learning and missing data imputation algorithms.

Co-training is a popular disagreement-based semi-supervised learning method. Learners of different views mutually select reliable unlabeled instances to augment the labeled dataset. Existing co-training style algorithms have cumbersome procedures for selecting confident instances. Furthermore, the pseudo-labels assigned to selected unlabeled instances are not always reliable. In this paper, we propose a safe co-training regression algorithm for multi-view scenarios with two characteristics. An instance selection strategy based on the consistency assumption aims to improve the efficiency of selecting confident unlabeled instances. This strategy makes full use of the information provided by a committee to measure the confidence of unlabeled instances. A safe labeling technique in an ensemble manner is introduced to improve the quality of pseudo-labels. The safe pseudo-labels not only integrate information provided by the committee, but also take into account the part of the receiver. The results over twenty datasets prove the superiority of the proposed algorithm against other state-of-the-art semi-supervised regression algorithms.

Semi-supervised regression attempts to utilize as much unlabeled data as possible and as little labeled data as possible to improve model performance. Methods based on data partitioning can improve regression performance from the perspective of data distribution. However, most partitioning methods only consider the correlation between the data, but not the relationship between the regressor and the data. In this study, a strategy of dividing the data and then regressing is proposed, where data partitioning is based on the relationship between the regression and data. Accordingly, an intuitive and effective algorithm named SRPF, i.e. Semi-supervised regression based on data partitioning and feature mapping, is proposed. First, we divide the labeled dataset into two disjoint subsets based on the relationship between the predicted and actual values of separation regressor. Second, we label these two subsets as distinct classes and use feature mapping to map data features to higher dimensions to better distinguish the data. Third, we construct a partitioner to determine which subset an unlabeled instance belongs to, and then use the regressor on the corresponding data set to make predictions. Finally, during the iterative training process, a self-training method is used to enrich labeled samples. Experiments are conducted on 15 well-known datasets compared to state-of-the-art algorithms. The results show that our method outperforms them in most datasets.

Player behavior modeling is of the utmost importance in game development and player matching. This problem is challenging because behavior is multi-semantic and hard to represent. Existing work often suffers from low generalization ability and high demand for supervisory information. In this paper, we present a behavior representation learning method (BRL) for Reversi players. It learns entirely from unlabeled game records. First, we develop an asymmetric encoder-decoder architecture to learn the mapping between states and actions. The encoder maps game records into a latent subspace for behavior representation. Second, we mask random states to force the encoder to capture the high-level features of the policy. The decoder predicts the corresponding actions according to the latent representation. Coupling these two designs, the semantic relevance of behaviors can be more effectively measured. Experiments were conducted in Reversi, using 14,000 game records of different players to learn behavior representations. Transfer performance in downstream tasks outperforms the supervised method and shows promising scaling ability. This work opens a new way for analyzing and modeling player behavior.

Label distribution learning (LDL) can more accurately represent the degree of correlation between labels and samples than multi-label learning. However, LDL usually has limited available label data, which is not conducive to training deep learning models. Data augmentation refers to methods for solving limited data problems by introducing unobserved data or latent variables to increase the size and quality of the training dataset. In this paper, we augment the dataset by mapping the features and label distributions of the generated samples to the same subspace, and using the generator to learn the distribution of the original data in this space. First, we use the encoder and generator to extract effective information from sample features and label distributions, respectively. Second, we randomly fuse the existing label distribution to generate a new label distribution, then map it to the subspace and restore the corresponding features through the decoder. Finally, these generated samples are mixed with the original training set to train a model for predicting label distribution. Experimental results on nine real-world datasets show that our proposed algorithm can improve the performance of deep learning models to a certain extent.

Real-world applications always contain complex data objects with multiple semantics. The machine learning tasks generalized from these applications can be formulated as multi-instance multi-label learning (MIML) problems. Each object is represented by a bag of multiple instances and can be associated with multiple labels simultaneously. Extensive studies on the MIML have been conducted during the past few years. However, many of them do not consider local label correlations, which is critical in multi-label scenario. To address this problem, we propose the multi-instance multi-label learning based on parallel attention and local label manifold correlation (MIML-LLMC) algorithm. First, parallel multiple attention mechanisms convert each bag into a number of fusion vectors, one for each label. They are also utilized to find out which instances in the bag trigger respective labels to improve explanability. Second, local label manifold correlation vectors (LLMCs) are constructed based on label manifold generation and clustering. They provide a high level of representation of the label vectors. Third, each fusion vector is concatenated to the LLMC to predict each label independently. With the utilization of attention and LLMCs, MIML-LLMC is able to discover instance-label relations and exploit local label correlations simultaneously. Experiments reveal that our approach is highly competitive to the state-of-the-art MIML algorithms and yields reasonable results in understanding the relations between input patterns and output label semantics.

As data dimensions and volume rapidly increase, attribute reduction using the original data becomes computationally infeasible. Large data frequently contain various redundant attributes and types of noise. This leads to the problems of overfitting and inefficiency in data processing. To address these problems, this paper proposes a general sampling method for attribute reduction by introducing three-way decisions, namely, three-way sampling (3WS), which is the first sampling method that describes the decision boundary accurately while improving the data quality significantly. To improve the effectiveness and efficiency of attribute reduction, we designed a rapid attribute reduction method based on three-way sampling (3WS-RAR). The 3WS-RAR method consists of three main steps: data sampling, attribute reduction, and model effectiveness evaluation. For data sampling, we define the three regions of the 3WS using support vectors to describe the data and use the boundary region as the sampling results. For the attribute reduction, we compute the neighborhood self-information for each attribute while considering the upper and lower approximations. For the effectiveness evaluation, we conducted experiments on 15 relatively large-scale datasets and analysed the influence of parameters. The experimental results reveal that, compared with state-of-the-art attribute reduction models, 3WS-RAR performs better on public benchmark datasets.

Significant place mining in spatiotemporal trajectory data is a key task for mobile pattern mining, useful for supporting location-aware services. State-of-the-art trajectory clustering algorithms utilize a density-based distance measure. However, some major problems with this approach are that (1) results are often inaccurate, especially on data of varying density, (2) the user must fine-tune many thresholds that are unintuitive to set, and (3) boundary points between clusters are often assigned to the wrong locations. Performance is also a major issue as many state-of-the-art algorithms have a very high time complexity. Motivated by these issues, this paper proposes an approach inspired by the data field theory and community detection. It is a graph-based significant place mining algorithm, called GB-SPM, for capturing and characterizing the essence of similarity between nodes. GB-SPM first applies a novel low index neighborhood velocity point filtration method to extract characteristic points. Then, a characteristic point index neighborhood is used to map them to graph nodes. In this way, the original problem is transformed into a community detection problem in complex community networks. Finally, a new edge weight metric is proposed to capture and characterize the nature of similarity between nodes. To evaluate clustering quality, we used the Silhouette (SI) for unannotated data to value inter-cluster separation and intra-cluster homogeneity. To evaluate mining effectiveness, we used Matthew’s correlation coefficient (MCC) for annotated data. Numerous experiments were carried out on real world datasets, and the accuracy and performance of the designed algorithm was compared with the state-of-the-art algorithms. Results show that GB-SPM improves on average SI by 13.9%, MCC by 20.7%, and runtime by 5.15 times.

Multi-instance learning (MIL) is more general and challenging than traditional supervised learning in that labels are given at the bag level. The popular feature mapping approaches convert each bag into an instance in the new feature space. However, most of them hardly maintain the distinguishability of bags, and the MIL model does not support self-reinforcement. In this article, we propose the multi-instance ensemble learning with discriminative bags (ELDB) algorithm with two new techniques. The bag selection technique obtains a discriminative bag set (dBagSet) according to two parts. First, considering the space and label distribution of the data, the bag selection process is optimized through discriminative analysis to obtain the basic dBagSet. Second, with the state and action transfer strategy, a dBagSet with better distinguishability is obtained through self-reinforcement. The ensemble technique trains a series of classifiers with these dBagSets and obtains the final weighted model. The experimental results show that ELDB is superior to the state-of-the-art MIL mapping solutions.

Clustering-based active learning approaches take advantage of the structure of the data to select representative instances. However, some algorithms are either inefficient or only applicable to some data. In this paper, we propose an effective and adaptive algorithm that will be called active learning through two-stage clustering (ALTA). The first stage is data preprocessing using the two-round-clustering algorithm to obtain $\sqrt n $ small blocks, where n is the number of instances. For each block, the closest instance of the center is selected as the representative. The second stage is the active learning of representative instances through density clustering. This stage consists of a number of iterations of density clustering, labeling and classification. In general, data preprocessing reduces the size of the data and the complexity of the algorithm. The combination of distance vector clustering and density clustering makes the algorithm more adaptive. Experiments are performed in comparison against the state-of-the-art active learning algorithms on nine datasets. Results demonstrate that the new algorithm has higher classification accuracy with the same number of labeled data.

Symbolic value partitioning is a knowledge reduction technique in the field of data mining. In this paper, we propose a granular computing approach for the partitioning task that includes granule construction and granule selection algorithms. The gra

Rough sets are efficient to extract rules from information systems. Matroids generalize the linear independency in vector spaces and the cycle in graphs. Specifically, matroids provide well-established platforms for greedy algorithms, while most existing algorithms for many rough set problems including attribute reduction are greedy ones. Therefore, the combination between rough sets and matroids may bring new efficient solutions to those important and difficult problems. In this paper, 2-circuit matroids, abstracted from matroidal characteristics of rough sets, are studied and axiomatized. A matroid is induced by an equivalence relation, and its characteristics including the independent set and duality are represented with rough sets. Based on these rough set representations, this special type of matroid is defined as 2-circuit matroids. Conversely, an equivalence relation is induced by a matroid, and its relationship with the above induction is further investigated. Finally, a number of axioms of the 2-circuit matroid are obtained through rough sets. These interesting and diverse axioms demonstrate the potential for the connection between rough sets and matroids.

Recently, pattern matching with flexible gap constraints has attracted extensive attention especially in biological sequence analysis and mining patterns from sequences. An issue is to search Maximal Pattern Matching with Gaps and the One-Off Condition (MPMGOOC). Firstly, we introduce the concept of MPMGOOC. In order to solve the problem, we propose some special concepts of Nettree which is different from a tree in that a node may have more than one parent. Based on Nettree, an algorithm named Heuristic Search Occurrence (HSO) is proposed. The space and time complexities of the algorithm are O(W*m*n) and O(W*n*(n+m*m)) respectively, where m, n, and W are the length of pattern P, sequence S and the maximal gap respectively. The comparison results show that HSO achieves better performance than a state-of-the-art algorithm in most cases of the real-world biological data testing.

Pattern matching is fundamental in applications such as biological sequence analysis and text indexing. A wildcard gap matches any subsequence with a length between two user specified integers, therefore introducing much adaptability to patterns. However, most existing works require that gaps in a pattern be the same. In this paper, we define a new pattern matching problem where gaps are independently specified. The objective is to compute the number of all matches. Since this number is exponential with respect to the maximal gap flexibility and the pattern length, counting matches one by one is computationally infeasible. We develop an efficient algorithm, named pattern matching with independent wildcard gaps (PAIG) for this problem, and propose two approaches to enhance its performance further. For the final version, the time complexity is O(Ll2W2), where L is the sequence length, l is the pattern length, and W is the maximal gap flexibility. The space complexity is O(lW), making PAIG easy to run in a Java Applet. Experimental results validate the efficiency of PAIG and confirm our analysis about its different versions.