A conditional opposition-based particle swarm optimization for feature selection

Abstract

Because of the existence of irrelevant, redundant, and noisy attributes in large datasets, the accuracy of a classification model has degraded. Hence, feature selection is a necessary pre-processing stage to select the important features that may considerably increase the efficiency of underlying classification algorithms. As a popular metaheuristic algorithm, particle swarm optimization has successfully applied to various feature selection approaches. Nevertheless, particle swarm optimization tends to suffer from immature convergence and low convergence rate. Besides, the imbalance between exploration and exploitation is another key issue that can significantly affect the performance of particle swarm optimization. In this paper, a conditional opposition-based particle swarm optimization is proposed and used to develop a wrapper feature selection. Two schemes, namely opposition-based learning and conditional strategy are introduced to enhance the performance of the particle swarm optimization. Twenty-four benchmark datasets are used to validate the performance of the proposed approach. Furthermore, nine metaheuristics are chosen for performance verification. The findings show the supremacy of the proposed approach not only in obtaining high prediction accuracy but also in small feature sizes.