Title: An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimisation

Authors: Fei Han; Shanxiu Yang; Jian Guan

Addresses: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China ' School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China ' School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China

Abstract: In this paper, a hybrid approach based on clustering and Particle Swarm Optimisation (PSO) is proposed to perform gene selection and classification for microarray data. In the new method, firstly, genes are partitioned into a predetermined number of clusters by K-means method. Since the genes in each cluster have much redundancy, Max-Relevance Min-Redundancy (mRMR) strategy is used to reduce redundancy of the clustered genes. Then, PSO is used to perform further gene selection from the remaining clustered genes. Because of its better generalisation performance with much faster convergence rate than other learning algorithms for neural networks, Extreme Learning Machine (ELM) is chosen to evaluate candidate gene subsets selected by PSO and perform samples classification in this study. The proposed method selects less redundant genes as well as increases prediction accuracy and its efficiency and effectiveness are verified by extensive comparisons with other classical methods on three open microarray data.

Keywords: gene selection; K-means clustering; particle swarm optimisation; PSO; extreme learning machine; microarray data; bioinformatics.

DOI: 10.1504/IJDMB.2015.071515

International Journal of Data Mining and Bioinformatics, 2015 Vol.13 No.2, pp.103 - 121

Received: 26 Feb 2014
Accepted: 03 Mar 2014
Published online: 31 Aug 2015 *

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