Optimized Feature Selection Using Modified Social Group Optimization

Abstract

This paper introduces binary variants of the Modified Social Group Optimization (MSGO) algorithm designed specifically for optimal feature subset selection in a wrapper-mode classification setting. While the original SGO was proposed in 2016 and later modified in 2020 to enhance its performance, it had not been previously applied to feature selection problems. MSGO represents an advancement over SGO, adept at efficiently exploring the feature space to identify optimal or near-optimal feature subsets through the minimization of a specified fitness function. The two newly proposed binary variants of MSGO are employed to identify the optimal feature combinations that maximize classification accuracy while minimizing the number of selected features. These binary algorithms are then compared against six established approaches and six state-of-the-art optimization algorithms to assess their performance. Various evaluation metrics are utilized across twenty-three datasets sourced from the UCI data repository to accurately judge and compare the efficacy of these algorithms. The experimental findings demonstrate the superiority of MSGO algorithms in effectively addressing feature selection problems.