Optimized Feature Selection Using Modified Social Group Optimization

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 modified in 2020 to enhance its performance, it was not 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 by minimizing 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. In these variants, the native MSGO is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. These binary algorithms are compared against six latest high-performing optimization 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 results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of the MSGO algorithm to search the feature space and select the most informative attributes for classification tasks. © 2024 Slovene Society Informatika. All rights reserved.