A Mixed Integer Programming Approach for Gene Selection

SHAO Lizhen; WANG Jieli; HU Guangda; LIU Jiwei

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A Mixed Integer Programming Approach for Gene Selection

SHAO Lizhen 1 * #,WANG Jieli 2,HU Guangda 1,LIU Jiwei 1

1.School of Automation, University of Science and Technology Beijing, Beijing 100083

2.School of Computer and Comminication Engineering, University of Science and Technology Beijing, Beijing 100083

*Correspondence author

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Funding: the National Natural Science Foundation of China （No.No. 81000650）, National High-tech Research Development Program of China (863 Program) （No.No. 2013AA040705）, specialized Research Fund for the Doctoral Program of Higher Education（No.No. 20100006120016）

Opened online:16 September 2013

Accepted by: none

Citation: SHAO Lizhen,WANG Jieli,HU Guangda.A Mixed Integer Programming Approach for Gene Selection[OL]. [16 September 2013] http://en.paper.edu.cn/en_releasepaper/content/4559213

%It is known that for most of gene expression data for cancer classification, the number of samples is quite small compared to the number of genes. Therefore, feature selection is an essential pre-processing step and a challenging problem to remove the irrelevant or redundant genes before classification.In this paper, we model the gene selection problem as a mixed integer programming problem based on 1-norm support vector machine (SVM). This problem is difficult to solve because the number of integer variables (usually tens of thousands or even hundreds of thousands) is very big compared to the desired number of genes. To solve this problem, we propose an iterative mixed integer optimization algorithm to gradually select a subset of genes. We test the proposed approach on colon cancer and leukemia cancer gene expression datasets. The results show that our proposed algorithm performs better than fisher criterion, T-statistics, standard 2-norm SVM and SVM recursive feature elimination (SVM-RFE) methods. The selected gene subset has better classification accuracy.

Keywords:Pattern recognition and intelligent system; Gene selection; Support Vector Machine; SVM-RFE; Mixed Integer Programming

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