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Energies 2016, 9(12), 1061; doi:10.3390/en9121061

Classification of Gene Expression Data Using Multiobjective Differential Evolution

School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
Author to whom correspondence should be addressed.
Academic Editor: Silvio Simani
Received: 6 September 2016 / Revised: 16 October 2016 / Accepted: 3 November 2016 / Published: 15 December 2016
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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. View Full-Text
Keywords: multiobjective method; differential evolution algorithm; binary differential evolution; binary optimization multiobjective method; differential evolution algorithm; binary differential evolution; binary optimization

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Ma, S.; Li, X.; Wang, Y. Classification of Gene Expression Data Using Multiobjective Differential Evolution. Energies 2016, 9, 1061.

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