吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1429-1434.doi: 10.7964/jdxbgxb201405033

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Novel method for microarray data dimension reduction

WANG Gang1,2,3, ZHANG Yu-xuan4, LI Ying1,2, CHEN Hui-ling5, HU Wei-tong6, QIN Lei1,2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3.College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;
    4.College of Communication Engineering, Jilin University, Changchun 130012, China;
    5.College of Physics and Electronic Information, Wenzhou University, Wenzhou 325035, China;
    6.Basic Course Department, Air Force Aviation University, Changchun 130022, China
  • Received:2013-07-05 Online:2014-09-01 Published:2014-09-01

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Abstract: A two stage parallel gene selection method (TPM) for obtaining the optimal feature subset is proposed. A fuzzy multi-swarm particle optimization (FMP) is also proposed to extend the searching spaces, to overcome the problem of traditional algorithm to be locked to local optimum. The performance of the TMP is evaluated on five microarray datasets (leukemia dataset, colon dataset, breast cancer dataset, lung carcinoma dataset and brain cancer dataset). The comparison results show that the proposed method not only gets better quality of feature subset and higher classification accuracy, but also generates smaller feature subsets. The results of this study could provide a new idea to the field of gene expression.

Key words: computer application, gene selection, feature selection, microarray, particle swarm optimization

CLC Number: 

  • TP301.6
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