An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. An equilibrium optimizer (EO) is a novel physics-based optimization algorithm; it was inspired by controlled volume mass balance models for estimating dynamic and equilibrium states. This article presents two binary equilibrium optimizer algorithm and for selecting the optimal feature subset for classification problems. The first algorithm maps the continuous EO into a discrete type using S-shaped and V-shaped transfer functions (BEO-S and BEO-V). The second algorithm is based on the position of the current optimal solution (target) and position vector (BEO-T). To verify the performance of the proposed algorithm, 19 well-known UCI datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary EO algorithms, BEO-V2 has the best comprehensive performance and has better performance than other state-of-the-art metaheuristic algorithms in terms of the performance measures.