Liknon feature selection for microarrays

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TypeBook Chapter
Proceedings titleApplications of Fuzzy Sets Theory : 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007. Proceedings
Series titleLecture Notes In Computer Science; Volume 4578
Conference7th International Workshop on Fuzzy Logic and Applications (WILF 2007), July 7-10, 2007. Camogli, Italy
Pages580587; # of pages: 8
SubjectFeature selection; gene expression microarray; linear programming; support vector machine; LIKNON; regularization parameter; sample to feature ratio
AbstractMany real-world classification problems involve very sparse and high-dimensional data. The successes of LIKNON - linear programming support vector machine (LPSVM) for feature selection, motivates a more thorough analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. Robust feature/model selection methods are desirable. LIKNON is claimed to have such robustness properties. Its feature selection operates by selecting the groups of features with large differences between the resultants of the two classes. The degree of desired difference is controlled by the regularization parameter. We study the practical value of LIKNON-based feature/model selection for microarray data. Our findings support the claims about the robustness of the method.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNRC Institute for Biodiagnostics; National Research Council Canada
Peer reviewedYes
NRC number2435
NPARC number9148116
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Record identifier17253e7d-ff9c-48ca-babe-6071dfd15081
Record created2009-06-25
Record modified2016-06-29
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