Gaussian based particle swarm optimisation and statistical clustering for feature selection

Abstract

Feature selection is an important but difficult task in classification, which aims to reduce the number of features and maintain or even increase the classification accuracy. This paper proposes a new particle swarm optimisation (PSO) algorithm using statistical clustering information to solve feature selection problems. Based on Gaussian distribution, a new updating mechanism is developed to allow the use of the clustering information during the evolutionary process of PSO based on which a new algorithm (GPSO) is developed. The proposed algorithm is examined and compared with two traditional algorithms and a PSO based algorithm which does not use clustering information on eight benchmark datasets of varying difficulty. The results show that GPSO can be successfully used for feature selection to reduce the number of features and achieve similar or even better classification performance than using all features. Meanwhile, it achieves better performance than the two traditional feature selection algorithms. It maintains the classification performance achieved by the standard PSO for feature selection algorithm, but significantly reduces the number of features and the computational cost.