Combining support vector machines and the t-statistic for gene selection in DNA microarray data analysis

Authors:
Tao Yang;Vojislave Kecman;Longbing Cao;Chengqi Zhang
Affiliations:
Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;Department of Computer Science, Virginia Commonwealth University, Richmond, VA;Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Venue:
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2010

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Abstract

This paper proposes a new gene selection (or feature selection) method for DNA microarray data analysis In the method, the t-statistic and support vector machines are combined efficiently The resulting gene selection method uses both the data intrinsic information and learning algorithm performance to measure the relevance of a gene in a DNA microarray We explain why and how the proposed method works well The experimental results on two benchmarking microarray data sets show that the proposed method is competitive with previous methods The proposed method can also be used for other feature selection problems.