Liknon Feature Selection for Microarrays

Authors:
Erinija Pranckeviciene;Ray Somorjai
Affiliations:
Institute for Biodiagnostics, National Research Council Canada, 435 Ellice avenue, Winnipeg, MB, R3B 1Y6,;Institute for Biodiagnostics, National Research Council Canada, 435 Ellice avenue, Winnipeg, MB, R3B 1Y6,
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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Abstract

Many 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.