The rapid advancement of technology has led to the generation of big data. This vast and diverse data can uncover valuable patterns and yield promising results when effectively mined, processed, and analyzed. However, it also introduces the “curse of dimensionality,” which can negatively impact the performance of machine learning models. Feature Selection (FS) is a data preprocessing technique aimed at identifying the optimal feature set to enhance model efficiency and reduce processing time. Numerous metaheuristic wrapper-based FS techniques have been explored in the literature. However, a significant drawback of many of these algorithms is their dependence on centralized learning, where the global best solution drives the search direction. This centralized approach is risky, as any error by the global best can hinder the exploration and exploitation of other potential areas, leading to inaccuracies in discovering the true global optimum. In this paper, the binary variant of a novel decentralized metaheuristic Kids Learning Optimization Algorithm (KLO) called Binary Kids Learning Optimization Algorithm (BKLO) is proposed for optimal feature selection for classification purposes in wrapper mode. The continuous solutions of KLO are converted to binary space by using the transfer function. A comparison is provided between the two transfer functions: hyperbolic tan (V-shaped) and the Sigmoidal (S-shaped) transfer functions. BKLO is compared with seven state-of-the-art algorithms. The performance of algorithms is evaluated and compared using several assessment indicators over fifteen benchmark datasets with a wide range of dimensions (small, medium, and large) from the University of California Irvine (UCI) repository and Arizona State University. The superiority of BKLO in reducing the number of features with increased classification accuracy over the other competing algorithms is demonstrated through the experiments and Friedman's Mean Rank (FMR) statistical tests.
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