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Published Articles >> Table of Contents >> Abstract
IEEE Computer Society Bioinformatics Conference (CSB'03)
p. 523
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
Chris Ding, University of California, Berkeley
Hanchuan Peng, University of California, Berkeley
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSB.2003.1227396
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| Abstract |
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Selecting a small subset of genes out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. Feature sets obtained through the minimum redundancy - maximum relevance framework represent broader spectrum of characteristics of phenotypes than those obtained through standard ranking methods; they are more robust, generalize well to unseen data, and lead to significantly improved classifications in extensive experiments on 5 gene expressions data sets.
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 Additional Information
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Citation:
Chris Ding, Hanchuan Peng,
"Minimum Redundancy Feature Selection from Microarray Gene Expression Data,"
csb,
p. 523,
IEEE Computer Society Bioinformatics Conference (CSB'03),
2003
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