title: Two-step cross-entropy feature selection for microarrays—power through complementarity.
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.
authors.affiliation: Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia. tpeters@efs.mq.edu.au
authors.forename: Tim
authors.initials: T
authors.lastname: Peters
authors.forename: David W
authors.initials: DW
authors.lastname: Bulger
authors.forename: To-Ha
authors.initials: TH
authors.lastname: Loi
authors.forename: Jean Yee Hwa
authors.initials: JY
authors.lastname: Yang
authors.forename: David
authors.initials: D
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D006801:Humans; D008198:Lymph Nodes / Q000737:chemistry / Q000473:pathology; D016393:Lymphoma, B-Cell / Q000235:genetics / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*