Many-Objective Jaccard-Based Evolutionary Feature Selection for High-Dimensional Imbalanced Data Classification.

Smart meters play a significant role in power systems, but their condition assessment faces challenges such as inconsistent evaluation criteria and inaccurate assessment results. This paper proposes feature engineering including feature construction and feature selection for smart meter group failure rate prediction. First, the basic structure and common fault types of smart meters are introduced. Smart meters are grouped by batch and distribution area. Next, 25 condition features are constructed based on failure mechanisms and technical specifications. Then, an evolutionary multi-objective feature selection algorithm combining NSGA-II, Jaccard similarity, and XGBoost is developed, where feature subsets are encoded as binary individuals optimized for three objectives: MSE, 1 − R2, and the number of features. The experimental results demonstrate that the proposed method not only reduces the number of features (25→7) but also improves the prediction accuracy (MSE: 0.0049 → 0.0042, R2: 0.6638 → 0.7228) of smart meter group failure rates. Comparative studies with other feature selection methods further confirm the superiority of our approach. The optimized features enhance interpretability and computational efficiency, providing a practical solution for large-scale smart meter condition assessment in power systems.

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.

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.

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.

Set-based integer-coded fuzzy granular evolutionary algorithms for high-dimensional feature selection

Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient () is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error () and maximum coefficient of determination (). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams.

In the case when conventional feature selection methods do not demonstrate sufficient performance, alternative algorithmic schemes might be applied. In this paper we propose an evolutionary feature selection technique based on the twocriteria optimization model. To diminish the drawbacks of genetic algorithms, which are used as optimizers, we design a parallel multi-criteria heuristic procedure based on an island model. The effectiveness of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the crucial aspects in the sphere of humanmachine communications. A number of multilingual corpora (German, English and Japanese) were engaged in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 11.15% relative improvement compared with the best F-score value on the full set of attributes).

Evolutionary feature selection on high dimensional data using a search space reduction approach

Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. Most of the population in our cities are exposed to high levels of noise that generate discomfort and different health problems. These issues may be mitigated by applying different smart cities solutions, some of them require high accurate noise information to provide the best quality of serve possible. In this study, we have designed a machine learning approach based on genetic algorithms to analyze noise data captured in the university campus. This method reduces the amount of data required to classify the noise by addressing a feature selection optimization problem. The experimental results have shown that our approach improved the accuracy in 20% (achieving an accuracy of 87% with a reduction of up to 85% on the original dataset).

Imbalanced data are known to be notably difficult to deal with, as it needs a thorough understanding of the data to know how it should be done. However, it occurs in many fields, especially in finance, like banking. Many problems that use financial data can be summarized as binary problems. Some of them are fatal if not identified correctly. This research aims to find how to utilize machine learning models to deal with imbalanced data, specifically one that contains binary classification problems. In this paper, we use imbalanced insurance and credit card datasets. The research is conducted by starting from doing feature selection in the datasets by removing irrelevant columns, followed by using SMOTE algorithm variants (K-Means SMOTE and Borderline SMOTE) and pure SMOTE algorithm as the oversampling methods, and Near-Miss and All-KNN for undersampling methods. The algorithms are implemented by using scikit libraries. Lastly, PCA is used for dimensionality reduction and Logistic Regression as the machine learning model with cross-validations for deciding the best hyperparameter. The procedure produces five different Logistic Regression models that differ in how it handles the imbalances, which will be compared. The result shows that the oversampling methods work better than undersampling methods, with K-Means SMOTE and Borderline SMOTE performing better than the pure SMOTE, meaning that machine learning can be used as a solution to deal with binary classification problems in imbalanced financial data.

The classification of high dimensional data is a challenging problem due to the presence of redundant and irrelevant features in a higher amount. These unwanted features degrade accuracy and increase the computational complexity of machine learning algorithms. In this paper, we propose a hybrid method that integrates the complementary strengths of feature selection and feature weighting approaches for improving the classification of high dimensional data on the Nearest Neighbor classifier. Specifically, we suggest four strategies that combine filter and wrapper methods of feature selection and feature weighting. Experiments are performed on 12 high dimensional datasets and outcomes are supported by Friedman as well as Holm statistical tests for validation. Extended Adjusted Ratio of Ratios is used to recognize the best method considering accuracy, feature selection, and runtime. The results show that two proposed strategies outperform other well-known methods in accuracy and features reduction. The hybrid feature selection-feature weighting wrapper method is the best among all in accuracy while the hybrid feature selection filter-feature weighting wrapper method is the most suitable for reducing features and runtime. Thus, the promising outcomes validate the importance of hybridizing feature selection and feature weighting while dealing with high dimensional data.

Exhaustive feature selection algorithms are optimal because all possible combinations of features are tested against a predetermined criterion. Suboptimal algorithms that trade performance for speed by considering only a subset of all feature combinations are generally preferred. An implementation of the exhaustive search feature selection (ESFS) method is described for the Bayes Gaussian statistics. The algorithm significantly reduces the computational and time requirements normally associated with optimal algorithms. The performance of this algorithm is compared to that of two suboptimal algorithms-forward sequential features selection and stepwise linear discriminant analysis. Results show that this implementation provides a moderate improvement in classification accuracy and is well suited for evaluating the performance of suboptimal algorithms.

Ensemble gene (feature) selection is a promising new strategy with many benefits including more stable gene lists and improved classification results. The ensemble portion is achieved through multiple runs of feature selection which are then aggregated into a single result. The critical question is how many iterations of feature selection are appropriate. Too few iterations can make classification performance suffer. However, too many iterations will cause issues regarding computational costs. The goal is to choose the correct number of iterations to maximize classification performance without expending too much computational power. Our paper is an in-depth study on the effect of the number of iterations of feature selection on classification performance. Our work employs eleven DNA microarray datasets, on which we apply various ensemble methods, feature selection techniques, classifiers, and feature subset sizes. The results show that using 10 iterations of feature ranking during ensemble feature selection is not sufficient to optimize classification results and that a larger number of iterations is required (20 or 50). However, there is very little distinction between 20 iterations and 50 iterations, as both produce very similar classification results. Our recommendation is to use 20 iterations because while 20 iterations and 50 iterations perform similarly, 20 iterations has a much smaller computation time. To our knowledge there has not been a previous study as expansive as this one on the effects of the number of iterations of feature selection on ensemble feature selection.