Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Background: Feature selection (FS), a crucial preprocessing step in machine learning, greatly reduces the dimension of data and improves model performance. This paper focuses on selecting features for medical data classification. Methods: In this work, a new form of ensemble FS method called weighted rank difference ensemble (WRD-Ensemble) has been put forth. It combines three FS methods to produce a stable and diverse subset of features. The three base FS approaches are Pearson’s correlation coefficient (PCC), reliefF, and gain ratio (GR). These three FS approaches produce three distinct lists of features, and then they order each feature by importance or weight. The final subset of features in this study is chosen using the average weight of each feature and the rank difference of a feature across three ranked lists. Using the average weight and rank difference of each feature, unstable and less significant features are eliminated from the feature space. The WRD-Ensemble method is applied to three medical datasets: chronic kidney disease (CKD), lung cancer, and heart disease. These data samples are classified using logistic regression (LR). Results: The experimental results show that compared to the base FS methods and other ensemble FS methods, the proposed WRD-Ensemble method leads to obtaining the highest accuracy value of 98.97% for CKD, 93.24% for lung cancer, and 83.84% for heart disease. Conclusion: The results indicate that the proposed WRD-Ensemble method can potentially improve the accuracy of disease diagnosis models, contributing to advances in clinical decision-making.

An effective bearing fault diagnosis model based on machine learning is proposed in this study. The model can separate into three stages: feature extraction, feature selection, and classification. In the stage of feature extraction, multiresolution analysis (MRA) and fast Fourier transform (FFT) are applied to extract the features from the raw signal measured from the rotating machine. In the second stage, a powerful feature selection method is proposed and utilized in the stage of feature selection. The new feature selection method is based on grey wolf optimization (GWO) and heap-based optimizer (HBO) with new strategies combined. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) are used as the classifier independently. To verify the capability of the proposed model, four different datasets are applied to test the model in this study, respectively University of California Irvine (UCI) benchmark dataset, bearing dataset, Case Western Reserve University (CWRU) benchmark dataset, and Machinery Failure Prevention Technology (MFPT) benchmark dataset. The proposed method is compared with the existing methods and can certify the robustness with the experiment results.

Optimised autoencoder-based ensemble deep learning approaches for cyber-physical event classification utilizing synchrophasor PMU data

Feature selection is a dimensionality reduction method known as a main step in data mining and machine learning. The aim of feature selection is to remove redundant and unrelated features. In recent years several feature selection methods based on graph theory and social networking techniques have been proposed .In this study, a feature selection approach based on multi objective PSO algorithm and social network techniques is presented. In the proposed method, Fisher score, node centrality and edge centrality are used to construct the fitness function in order to present a multi objective particle swarm optimization (PSO) approach. The proposed method run over a variety of datasets and the results are compared with the well-known filter-based feature selection methods. The results show that the proposed method is effective and the performance of the proposed method better than other methods in some cases.

Type 2 diabetes is a well-known lifelong condition disease that reduces the human body’s ability to produce insulin. This causes high blood sugar levels, which leads to different complications, including stroke, eye, cardiovascular, kidney, and nerve damage. Although diabetes has attained the attention of huge research, the classification performance of such medical problems utilizing techniques of machine learning is quite low, primarily due to the class imbalance and the presence of missing values in data. In this work, we proposed a model using binary Grey wolf optimization (GWO) and a Decision tree. The proposed model is composed of preprocessing, feature selection, and classification. In preprocessing, that is responsible for minority class oversampling and handling missing values. In the second step, binary GWO are used to select the most significant features. In the third step, the proposed model is trained using the Decision tree algorithm. The model achieved an accuracy of 83.11% when it was applied on the Pima Indian`s dataset.

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.

Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in local optima areas may still be a concern. Several significant GWO factors can be explored to enhance the performance of selection in classification, with two conflicting concepts to be considered in using or modeling a metaheuristic method, exploring a search field, and exploiting optimal solutions. Balancing exploration and exploitation in a good manner will improve the search algorithm's performance. To achieve a good balance, this paper proposes a binary hybrid GWO and Harris Hawks Optimization (HHO) to form a memetic approach called HBGWOHHO. The sigmoid transfer function is used to transfer the continuous search space into a binary one to meet the feature selection nature requirement. A wrapper-based k-Nearest neighbor is used to evaluate the goodness of the selected features. To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. The findings revealed that the proposed method was effective in improving the performance of the BGWO algorithm. The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. Similarly, compared with BPSO and BGA feature selection algorithms, the proposed HBGWOHHO surpassed them yield better accuracy, the smaller size of selected features in much lower computational time.

This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms.

In classifying diseases, many datasets have a number of redundant features that do not affect classification accuracy. There are several evolutionary algorithms that are used to determine the feature and reduce dimensional patterns such as the gray wolf optimization (GWO) and the firefly algorithm (FFA). In this paper, a hybrid optimization algorithm BGWO_FFA was proposed to find the optimal subset of datasets. BGWO_FFA benefits from the ability of both GWO and FFA to find the best subset of features in search space. The results show that BGWO_FFA significantly outperformed the results of the algorithm BGWO as it showed efficient and high accuracy through the mean squared error (MSE) and the number of features selection.

Heart disease is still one of the top causes of death globally, demanding early and effective prediction tools. Traditional predictive models frequently have limitations because they rely on single‐source statistics, which may not represent the entire scenario of a patient's complex health profile. Feature selection is an essential preprocessing step in machine learning that improves model efficiency by eliminating irrelevant or redundant features. This study evaluates the impact of feature selection on six classification modelsNaïve Bayes, Logistic Regression, Decision Tree, K‐Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forestacross four datasets. Performance was assessed using accuracy, precision, recall, F1‐score, and area under the curve (AUC) before and after feature selection. The results indicate that feature selection had varying effects across different models and datasets. In Dataset‐01, Random Forest maintained its strong performance (98.70% accuracy) even after feature selection, while SVM and KNN saw noticeable drops, with accuracy decreasing from 88.96% to 79.87% and 87.01% to 81.17% respectively. For Dataset‐02, SVM showed improvement, increasing from 80.00% to 85.00%, while Naïve Bayes and KNN also improved, reaching 82.50% and 82.50% accuracy, respectively. In Dataset‐03, Logistic Regression and KNN experienced declines, with accuracy dropping from 88.76% to 83.71% and 91.57% to 87.08%, respectively, while Random Forest fell from 97.75% to 91.57%. Dataset‐04 showed mixed results, with Logistic Regression improving from 86.67% to 91.11%, but KNN and Random Forest dropping from 97.78% to 84.44% and 91.11% to 82.22%, respectively. These findings suggest that while feature selection enhances performance for some models, it can negatively impact others, particularly those reliant on a larger feature set. Future research should explore adaptive feature selection techniques to optimize model performance across diverse datasets.

Feature Selection, one of the most important preprocessing steps in Machine Learning, is the process where we automatically or manually select those features which contribute most to our prediction variable or output in which we are interested. This subset of features has some very important benefits: it reduces the computational complexity of learning algorithms, saves time, improves accuracy, and the selected features can be insightful for the people involved in the problem domain. Among the different ways of performing feature selection such as filter, wrapper and hybrid, filter-based separability methods can be used as a feature ranking tool in binary classification problems, most popular being the Bhattacharya distance and the Jeffries–Matusita (JM) distance. However, these measures are parametric and their computation requires knowledge of the distribution from which the samples are drawn. In real life, we often come across instances where it is difficult to have an idea about the distribution of observations. In this paper, we have presented a new non-parametric approach for performing feature selection called the ‘Non-Parametric Distance Measure’. The experiment with the new measure is performed over nine datasets and the results are compared with other ranking-based methods for feature selection using those datasets. This experiment proves that the new box-plot-based method can provide greater accuracy and efficiency than the conventional ranking-based measures for feature selection such as the Chi-Square, Symmetric Uncertainty and Information Gain.

Feature selection is an important preprocessing step in machine learning and pattern recognition where in the former it is aimed at removing some irrelevant and/or redundant features from a given dataset. In this paper, a new graph theoretic based feature selection method is proposed. The proposed method uses the social network techniques to select the final feature set. In other word, the community detection algorithm with the node centrality measure are integrated for the feature selection problem. Furthermore, this method can be applied on both supervised and unsupervised modes. We also compared the performance of the proposed method with the well-known and state-of-the-art filter based feature selection methods. The results indicate that the efficiency and effectiveness of the proposed method as well as improvements over previous related methods can be seen.

Neighborhood effective information ratio for hybrid feature subset evaluation and selection

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study.

Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is proposed to select the optimal feature subset from a large number of features extracted from radar emitter signals (RESs). The criterion function is given firstly. Then, detailed algorithm of QGA is described and its performances are analyzed. Finally, the best feature subset is selected from the original feature set (OFS) composed of 16 features of RESs. Experimental results show that the proposed approach reduces greatly the dimensions of OFS and heightens accurate recognition rate of RESs, which indicates that NFSA is feasible and effective.