Chaotic maps in binary particle swarm optimization for feature selection

Abstract

Feature selection is a useful pre-processing technique for solving classification problems. The challenge of using evolutionary algorithms lies in solving the feature selection problem caused by the number of features. Classification data may contain useless, redundant or misleading features. To increase the classification accuracy, the primary objective is to remove irrelevant features in the feature space and identify the relevant features. Binary particle swarm optimization (BPSO) has been applied successfully in solving feature selection problem. In this paper, two kinds of chaotic maps are embedded in binary particle swarm optimization (BPSO), a logistic map and a tent map, respectively. The purpose of the chaotic maps is to determine the inertia weight of the BPSO. In this study, we propose the chaotic binary particle swarm optimization (CBPSO) method to implement feature selection, and the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as a classifier to evaluate the classification accuracies. The proposed method showed promising results for feature selection with respect to the number of feature subsets. The classification accuracy obtained by the proposed method is superior to ones obtained by the other methods from the literature.