Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity

Authors:
Tim Peters;David W. Bulger;To-ha Loi;Jean Yee Hwa Yang;David Ma
Affiliations:
Macquarie University, Sydney;Macquarie University, Sydney;St. Vincent's Hospital and St. Vincent's Centre for Applied Medical Research, Sydney;University of Sydney, Sydney;St. Vincent's Hospital and St. Vincent's Centre for Applied Medical Research, Sydney
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2011

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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 [CHECK END OF SENTENCE]. Using a feature selection method with the computational architecture of the cross-entropy method [CHECK END OF SENTENCE], 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.