Particle Swarm Optimisation with genetic operators for feature selection

Abstract

Feature selection is an important task in machine learning, which aims to reduce the dataset dimensionality while at least maintaining the classification performance. Particle Swarm Optimisation (PSO) has been widely applied to feature selection because of its effectiveness and efficiency. However, since feature selection is a challenging task with a complex search space, PSO easily gets stuck at local optima. This paper aims to improve the PSO's searching ability by applying genetic operators such as crossover and mutation to assist the swarm to explore the search space better. The proposed genetic operators are specifically designed for feature selection, which not only improve the quality of current feature subsets but also make the search smoother. The proposed algorithm, called CMPSO, is tested and compared with three recent PSO based feature selection algorithms. Experimental results on eight datasets show that CMPSO can adapt with different numbers of features to evolve small feature subsets, which achieve similar or better classification performance than using all features and the three PSO based algorithms. The analysis on evolutionary processes shows that genetic operators assist CMPSO to evolve better solutions than the original PSO.