Abstract
In this paper, a novel hybrid approach based on clustering and particle swarm optimization (PSO) is proposed for gene selection and classification of microarray data. In this approach, PSO combining with clustering method are used to perform gene selection to reduce redundancy. Firstly, genes are partitioned into a certain number of clusters by using K-means, and then PSO is used to perform gene selection from the clustered genes. Because of its better generalization performance with much faster convergence rate than other learning algorithms for neural networks, extreme learning machine (ELM) is chosen to perform sample classification in the hybrid method. The proposed method selects less redundant interpretable genes as well as increases prediction accuracy. The efficiency and effectiveness of the proposed method is verified by extensive comparisons with other classic methods on some open microarray data.
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Yang, S., Han, F., Guan, J. (2013). A Hybrid Gene Selection and Classification Approach for Microarray Data Based on Clustering and PSO. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_15
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DOI: https://doi.org/10.1007/978-3-642-39678-6_15
Publisher Name: Springer, Berlin, Heidelberg
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