关键词: 基因选择, 微阵列数据, 聚类, 微粒群优化, 极限学习机

Abstract: Since traditional gene selection methods may select a large number of irrelevant genes, which leads to low sample prediction accuracy, a new hybrid method based on clustering and Particle Swarm Optimization (PSO) was proposed for gene selection of microarray data in this paper. Firstly, genes were partitioned into a certain number of clusters by using clustering algorithm. Then Extreme Learning Machine (ELM) was applied to validate the classification performance of the genes selected from each cluster, which formed an initial gene pool. Finally, the wrapper approach based on PSO and ELM was used to select compact gene subset with high classification accuracy from the initial gene pool. The better classification accuracy on microarray data was provided with the genes selected by the proposed method. The experiments on two public microarray data sets verify that the proposed method can obtain better classification performance with fewer genes than other classical methods.

Key words: gene selection, microarray data, clustering, Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM)