Abstract

In Machine Learning (ML) tasks, Feature Selection (FS) methods are typically used by learning machines to filter data prior to training to lessen training complexity, thus alleviating the challenge of dimensional catastrophe. FS is a combinatorial optimization problem that can be used with meta-heuristics to better select the optimum combination of relevant features to help ML tasks provide adequate classification accuracy. White Shark Optimizer (WSO), as a swarm intelligence algorithm, has been extensively applied to address optimization problems with diverse levels of complexity. Although WSO is a promising meta-heuristic, it occasionally exhibits low solution accuracy and slow pace of convergence in addressing optimization tasks. These critical issues could be significantly curtailed by carefully balancing exploratory and exploitative mechanisms in WSO. In this, three binary variants of WSO were proposed, namely: (1) Binary Adaptive WSO (BAWSO) which assesses population distribution and estimates evolutionary status, (2) Binary Comprehensive Learning WSO (BCLWSO) which uses all the search agents' best solutions and which may act as a model for velocity updating, and (3) Binary Heterogeneous CLWSO (BHCLWSO) which splits the population into two subpopulations in order to undertake exploration and exploitation separately and without interfering with each other. Further, a Binary variant of the classical WSO (BWSO) was created alongside these variants. A thorough evaluation of these algorithms was conducted using 24 well-known datasets. The findings showed that the proposed algorithms significantly boosted the performance level of BWSO and showed that BHCLWSO outperformed other binary algorithms in classification accuracy and number of selected features.