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IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul-Aug;8(4):1148-51. doi: 10.1109/TCBB.2011.30.

Two-step cross-entropy feature selection for microarrays—power through complementarity.

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  • 1Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia. tpeters@efs.mq.edu.au

Abstract

Current feature selection methods for supervised classification of tissue samples from microarray data generally fail to exploit complementary discriminatory power that can be found in sets of features. Using a feature selection method with the computational architecture of the cross-entropy method, including an additional preliminary step ensuring a lower bound on the number of times any feature is considered, we show when testing on a human lymph node data set that there are a significant number of genes that perform well when their complementary power is assessed, but “pass under the radar” of popular feature selection methods that only assess genes individually on a given classification tool. We also show that this phenomenon becomes more apparent as diagnostic specificity of the tissue samples analysed increases.

PMID:
21321369
[PubMed - indexed for MEDLINE]
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