Minimum Redundancy Feature Selection from Microarray Gene Expression Data

Authors:
Chris Ding;Hanchuan Peng
Affiliations:
-;-
Venue:
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Year:
2003

Citing 9
Cited 63

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Abstract

Selecting a small subset of genes out of the thousands ofgenes in microarray data is important for accurate classificationof phenotypes. Widely used methods typically rank genesaccording to their differential expressions among phenotypesand pick the top-ranked genes. We observe that feature sets soobtained have certain redundancy and study methods to minimizeit. Feature sets obtained through the minimum redundancy- maximum relevance framework represent broader spectrumof characteristics of phenotypes than those obtained throughstandard ranking methods; they are more robust, generalizewell to unseen data, and lead to significantly improved classificationsin extensive experiments on 5 gene expressions datasets.