Particle Swarm Optimisation for Feature Selection: A Size-Controlled Approach

Butler-Yeoman, T., Xue, B. and Zhang, M.

    Feature selection is a preprocessing step in classification tasks, which can reduce the dimensionality of a dataset and improve the classification accuracy and efficiency. However, many current feature selection algorithms select an unnecessarily large feature sub- sets, particularly on datasets with high dimensionality. This paper proposes a new particle swarm optimisation (PSO) based feature selection approach, where a new method is proposed to find the possible smallest size that potentially good feature subsets can have to guide the PSO algorithm to search for smaller feature subsets. The proposed algorithm is examined and compared with original PSO based feature se- lection and two typical feature selection method on twelve benchmark datasets of varying difficulty. The experimental results show that the proposed algorithm successfully further reduces the dimensionality of the dataset over original PSO and one of the conventional method, and maintains or even increases the classification performance in most cases. The proposed algorithm selects more features than the other conventional method, but achieves better classification performance in most cases, which shows that the proposed algorithm can balance the classification performance and the number of features in most cases. Furthermore, the proposed algorithm also shows better efficiency and consistency performance in terms of selecting consistent features across different stochastic runs.
Cite as: Butler-Yeoman, T., Xue, B. and Zhang, M. (2015). Particle Swarm Optimisation for Feature Selection: A Size-Controlled Approach. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 151-159
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