Evolutionary feature selection for emotion recognition in multilingual speech analysis

Filters and wrappers represent two mainstream approaches to feature selection (FS). Although evolutionary wrapper-based FS outperforms filters in addressing real-world classification problems, extending these methods to high-dimensional, many-objective optimization problems with imbalanced data poses substantial challenges. Overcoming computational costs and identifying suitable performance metrics are vital for navigating search operation complexities. Here, we propose using the Jaccard similarity (JS) in a set-based evolutionary many-objective (JSEMO) FS search, addressing both evolutionary FS and imbalanced classifier choice concurrently. This study highlights the mutual influence between these aspects, impacting overall algorithm performance. JSEMO integrates JS into population initialization, reproduction, and elitism steps, enhancing diversity and avoiding duplicate solutions. The set-based variation operator utilizes intersection and union operators for compatibility with binary coding. We also introduce a double-weighted KNN (KNN2W) classifier with four supportive objectives as a many-objective FS problem to handle imbalanced distributions. Compared with 20 methods on 15 benchmark problems, JSEMO produces distinct optimal features, significantly improving overall accuracy, balance accuracy, and g-mean metrics with comparable feature set size and computational cost. The ablation study underscores the positive impact of all JSEMO components, highlighting the set-based variation operation with JS and KNN2W with relevant evaluation metrics as the most influential aspects.

Designing efficient learning models capable of dealing with tons of data has become a reality in the era of big data. However, the amount of available data is too much for traditional data mining techniques to be applicable. This issue is even more serious when evolutionary algorithms are a key part of the learning algorithm. In this scenario, one typical approach is to follow a divide-and-conquer strategy, where data is divided into different chunks that are individually and independently addressed. Afterwards, the partial knowledge obtained from each chunk of data is combined in order to give a solution to the problem. Nevertheless, these kinds of local approaches do not look at data as a whole, missing a global view of the problem, which may result in less accurate models that also depend on how data is split. In this work, we focus on evolutionary feature selection algorithms. A divide-and-conquer approach to handle evolutionary feature selection in big data was already developed. We aim at designing its global counterpart, which looks at the feature selection problem from a global perspective, making use of the data as a whole to select the most appropriate features. In order to do so, we consider Apache Spark as a big data technology where our algorithm is implemented. We design a genetic algorithm capable of dealing with big datasets by selecting the proper parameters for our base algorithm (the well-known CHC) and adapting the evaluation procedure to take all the distributed data into account. Several preliminary results are discussed to study the feasibility of global evolutionary feature selection methods for big datasets.

Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes) implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.

Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks

Type 1 Diabetes Mellitus (T1D) has been increasingly associated with alterations in the gut microbiome. However, the impact of taxonomic resolution, feature selection strategies, and machine learning methods on microbiome-based prediction remains incompletely understood. We analyzed publicly available 16S rRNA gene sequencing datasets from two geographic cohorts to evaluate microbiome-based prediction of T1D. Microbial features were constructed at multiple taxonomic levels and as full hierarchical taxonomic paths preserving phylogenetic structure. Machine learning models were trained using stratified cross-validation and cross-cohort validation frameworks. Feature selection was performed using Binary Particle Swarm Optimization (BPSO) to identify compact and predictive microbial signatures. Model performance was evaluated using AUC, Accuracy, F1 score, and Matthews Correlation Coefficient. Differential abundance analysis using the LinDA framework was used to support biological interpretation of selected taxa. Tree-based models, particularly Random Forest and XGBoost, achieved the strongest predictive performance across taxonomic representations. Taxonomic resolution influenced model behavior, with family-level features providing strong performance with compact feature sets, while higher-resolution representations did not consistently improve performance despite increased complexity. BPSO identified consistently selected taxa across validation frameworks, suggesting stable predictive signatures. Several of these taxa have been linked to inflammatory or metabolically altered gut environments. Cross-cohort validation showed reduced performance compared with within-study models, highlighting challenges in generalization. Machine learning combined with BPSO-based feature selection provides an effective framework for identifying predictive microbial signatures associated with T1D. Our findings highlight the importance of taxonomic resolution, feature stability, and cross-cohort validation in microbiome-based predictive modeling. Integrating evolutionary feature selection with machine learning and biological validation may improve the robustness and interpretability of candidate microbial signatures.

Feature selection is a technique used to reduce an original set of features to a subset containing the most salient features. Reducing the feature set to the most significant subset of features typically results in an increase in the overall accuracy of a system. It has been shown that in some cases, the use of feature selection can make an underlying system susceptible to adversarial attacks. In this paper, we investigate the susceptibility of a feature selection-based Authorship Attribution System (AAS) to adversarial authorship attacks. The AAS studied is an instance of a Linear Support Vector Machine (LSVM). The feature selection algorithm used is an instance of Genetic & Evolutionary Feature Selection (GEFeS)In order to evaluate the GEFeS+LSVM-based AAS, we use three adversarial authorship masking techniques to generate adversarial texts to attack the AAS. Our results show that in some cases the GEFeS+LSVM-based AAS is more susceptible to adversarial authorship attacks. We provide a simple measurement to determine whether the use of GEFeS is beneficial or detrimental to a LSVM-based AAS.

The connection between emotional states and physical health has attracted widespread attention. The emotional stress assessment can help healthcare professionals figure out the patient's engagement toward the diagnostic plan and optimize the rehabilitation program as feedback. It is of great significance to study the changes of physiological features in the process of emotional change and find out subset of one or several physiological features that can best represent the changes of psychological state in a statistical sense. Previous studies had used the differences in physiological features between discrete emotional states to select feature subsets. However, the emotional state of the human body is continuously changing. The conventional feature selection methods ignored the dynamic process of an individual's emotional stress in real life. Therefore, a dedicated experimental was conducted while three peripheral physiological signals, i.e., ElectroCardioGram (ECG), Galvanic Skin Resistance (GSR), and Blood Volume Pulse (BVP), were continuously acquired. This paper reported a novel feature selection method based on emotional state transition, the experimental results show that the number of physiological features selected by the proposed method in this paper is 13, including 5 features of ECG, 4 features of PPG and 4 features of GSR, respectively, which are superior to PCA method and conventional feature selection method based on discrete emotional states in terms of dimension reduction. The classification results show that the accuracy of the proposed method in emotion recognition based on ECG and PPG is higher than the other two methods. These results suggest that the proposed method can serve as a viable alternative to conventional feature selection methods, and emotional state transition deserves more attention to promote the development of stress assessment.

In machine learning, feature selection is a commonly used technique for improving the predictive performance and interpretability of a trained model. Feature selection techniques are classified into three approaches: the filter, wrapper, and embedded approaches. The embedded approach performs the feature selection process during the model training and achieves a good balance between performance and computational cost in general. In the paper, we propose a novel embedded feature selection method using probabilistic model-based evolutionary optimization. We introduce the multivariate Bernoulli distribution, which determines the selection of features, and we optimize its parameters during the training. The distribution parameter update rule is the same as that of the population-based incremental learning (PBIL), but we simultaneously update the parameters of the machine learning model using an ordinary gradient descent method. This method can be easily implemented into non-linear models, such as neural networks. Moreover, we incorporate the penalty term into the objective function to control the number of selected feature. We apply the proposed method with the neural network model to the feature selection of three classification problems. The proposed method achieves competitive performance and reasonable computational cost compared with conventional feature selection methods.

This paper presents the application of FeaSANNT, an evolutionary algorithm for optimization of artificial neural networks, to the training of a multi-layer perceptron for identification of defects in wood veneer. Given a fixed artificial neural network structure, FeaSANNT concurrently evolves the input feature vector and the network weights. The novelty of the method lies in the implementation of the embedded approach in an evolutionary feature selection paradigm. Experimental tests show that the proposed algorithm produces high-performing solutions with robust learning results. A significant reduction of the set of veneer features is obtained. Experimental comparisons are made with a previous method based on statistical filtering of the input features and a standard genetic wrapper algorithm. In the first case, FeaSANNT greatly reduces the feature set with no degradation of the neural network accuracy. Moreover, FeaSANNT entails lower design costs, since feature selection is fully automated. In the second case, the proposed algorithm achieves superior results in terms of identification accuracy and reduction of the feature set. FeaSANNT involves also lower computational costs than the standard evolutionary wrapper approach and eases the algorithm design effort. Limited overlapping is observed between the patterns of features selected by the three algorithms. This result suggests that the full feature set contains mainly redundant attributes.

Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class imbalance problem, thus the selected features will be biased towards the majority class. Considering that F-measure is a more reasonable performance measure than accuracy for imbalanced data, this paper presents an effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures. Since F-measure optimization can be decomposed into a series of cost-sensitive classification problems, we investigate the cost-sensitive feature selection by generating and assigning different costs to each class with rigorous theory guidance. After solving a series of cost-sensitive feature selection problems, features corresponding to the best F-measure will be selected. In this way, the selected features will fully represent the properties of all classes. Experimental results on popular benchmarks and challenging real-world data sets demonstrate the significance of cost-sensitive feature selection for the imbalanced data setting and validate the effectiveness of the proposed method.

This paper reports on the primary decision problem in the smart TV UI design -- features selection. The conventional feature selection methods were not able to render what features are to be included in the new TV design, and this position paper introduced the False Belief to extract a users' schema of the novel features from smart TVs. We found that this technique could effectively examine the users' mental model of the smart TV services, in a rather unconscious way at the expense of extra training time. Also, it can quantify utility values of the features so that the designer can prioritize what features should be firstly considered in a novel product development.

Low-rank learning for feature selection in multi-label classification

This paper proposes anomaly detection for hydroelectric generating units by Fast Robust Random Cut Forest with a fast feature selection method by considering characteristics of operating data and Random Cut Trees. Hydroelectric generating units are renewable electric generating sources for electricity supply. Therefore, it is crucial to accurately detect anomalies of hydroelectric generating units. Moreover, effective features for anomaly detection should be selected to reduce operation costs of anomaly detection services. The proposed anomaly detection method can detect anomalies approximately thirty times faster than the conventional Robust Random Cut Forest with an accuracy comparative to that of the conventional method. The proposed feature selection method can select effective features almost fifty times faster than the conventional feature selection method.

Class-Specific feature selection for enhanced cattle behavior recognition using wearable-based system

© 2020 by the authors. The main challenge of classification systems is the processing of undesirable data. Filter-based feature selection is an effective solution to improve the performance of classification systems by selecting the significant features and discarding the undesirable ones. The success of this solution depends on the extracted information from data characteristics. For this reason, many research theories have been introduced to extract different feature relations. Unfortunately, traditional feature selection methods estimate the feature significance based on either individually or dependency discriminative ability. This paper introduces a new ensemble feature selection, called fuzzy feature selection based on relevancy, redundancy, and dependency (FFS-RRD). The proposed method considers both individually and dependency discriminative ability to extract all possible feature relations. To evaluate the proposed method, experimental comparisons are conducted with eight state-of-the-art and conventional feature selection methods. Based on 13 benchmark datasets, the experimental results over four well-known classifiers show the outperformance of our proposed method in terms of classification performance and stability.