Feature Selection Methods in Twin Support Vector Machines

Abstract

For the development of a machine learning model both parameter tuning and feature selection are necessary . The model�s hyper parameters need to be tuned toachieve the best values because they have a significant impact on how well themodel works and the objective of feature selection is to identify the most important subset of features that contribute to reliable predictions and model understanding.The primary goal of this study is to examine the effectiveness of feature selectiontechniques when used Twin Support Vector Machines (TWSVM) and traditionalSupport Vector Machines (SVM). We want to determine that the feature selectiontechnique results is the best performance increase for TWSVM and SVM by conducting extensive experiments on multiple datasets. The results of this study willgive important information about how feature selection will improve the classification accuracy and effectiveness.The methodology used in this study involves applying different kinds of parameter tuning and feature selection techniques for Support Vector Machines (SVM)and Twin Support Vector Machines (TWSVM) using linear and RBF kernels. Weused a hybrid approach to parameter tuning and feature selection. Optimized thehyper parameters using the Grid Search and Simulated Annealing (SA) methods.Then, with SA-based parameter tuning, we combined the Binary GravitationalSearch Algorithm (BGSA) and Teaching-Learning-Based Optimization (TLBO) forfeature selection.We use these techniques to enhance the performance of SVM and TWSVM modelsby tuning their parameters and selecting useful features. Our results show thatfeature selection methods are more effective at selecting relevant features whileusing less computation time in TWSVM compare to SVM.