Classification of Gene Expression Data Using Multiobjective Differential Evolution

Shijing Ma, Xiangtao Li and Yunhe Wang
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Shijing Ma: School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
Xiangtao Li: School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
Yunhe Wang: School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China

Energies, 2016, vol. 9, issue 12, 1-22

Abstract: Gene expression data are usually redundant, and only a subset of them presents distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in bioinformatics. In this paper, a multiobjective binary differential evolution method (MOBDE) is proposed to select a small subset of informative genes relevant to the classification. In the proposed method, firstly, the Fisher-Markov selector is used to choose top features of gene expression data. Secondly, to make differential evolution suitable for the binary problem, a novel binary mutation method is proposed to balance the exploration and exploitation ability. Thirdly, the multiobjective binary differential evolution is proposed by integrating the summation of normalized objectives and diversity selection into the binary differential evolution algorithm. Finally, the MOBDE algorithm is used for feature selection, and support vector machine (SVM) is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effectiveness and efficiency of the algorithm, the proposed method is tested on ten gene expression datasets. Experimental results demonstrate that the proposed method is very effective.

Keywords: multiobjective method; differential evolution algorithm; binary differential evolution; binary optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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