An experimental comparison of gene selection by Lasso and Dantzig selector for cancer classification

Authors:
Songfeng Zheng;Weixiang Liu
Affiliations:
Department of Mathematics, Missouri State University, 901 S. National Ave., Springfield, MO 65897, USA;Biomedical Engineering Lab, School of Medicine, Shenzhen University, Shenzhen, Guangdong 518060, China
Venue:
Computers in Biology and Medicine
Year:
2011

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Abstract

Selecting a subset of genes with strong discriminative power is a very important step in classification problems based on gene expression data. Lasso and Dantzig selector are known to have automatic variable selection ability in linear regression analysis. This paper applies Lasso and Dantzig selector to select the most informative genes for representing the probability of an example being positive as a linear function of the gene expression data. The selected genes are further used to fit different classifiers for cancer classification. Comparative experiments were conducted on six publicly available cancer datasets, and the detailed comparison results show that in general, Lasso is more capable than Dantzig selector at selecting informative genes for cancer classification.