A novel metric for redundant gene elimination based on discriminative contribution

Authors:
Xue-Qiang Zeng;Guo-Zheng Li;Jack Y. Yang;Mary Qu Yang
Affiliations:
Institute of System Biology, Shanghai University, Shanghai, China and School of Computer Engineering and Science, Shanghai University, Shanghai, China;Institute of System Biology, Shanghai University, Shanghai, China and School of Computer Engineering and Science, Shanghai University, Shanghai, China;Harvard Medical School, Harvard University, Cambridge, Massachusetts;National Human Genome Research Institute National Institutes of Health, U.S. Department of Health and Human Services Bethesda, MD
Venue:
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Year:
2008

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Abstract

As a high dimensional problem, analysis of microarray datasets is a hard task, where many weakly relevant but redundant featureshurt generalization performance of classifiers. There are previous worksto handle this problem by using linear or nonlinear filters, but thesefilters do not consider discriminative contribution of each feature by utilizingthe label information. Here we propose a novel metric based ondiscriminative contribution to perform redundant feature elimination.By the new metric, complementary features are likely to be reserved,which is beneficial for the final classification. Experimental results onseveral microarray data sets show our proposed metric for redundantfeature elimination based on discriminative contribution is better thanthe previous state-of-arts linear or nonlinear metrics on the problem ofanalysis of microarray data sets.