On the Use of Variable Complementarity for Feature Selection in Cancer Classification
The paper presents an original filter approach for effective feature selection in classification tasks with a very large number of input variables. The approach is based on the use of a new information theoretic selection criterion: the double input symmetrical relevance (DISR). The rationale of the criterion is that a set of variables can return an information on the output class that is higher than the sum of the informations of each variable taken individually. This property will be made explicit by defining the measure of variable complementarity. A feature selection filter based on the DISR criterion is compared in theoretical and experimental terms to recently proposed information theoretic criteria. Experimental results on a set of eleven microarray classification tasks show that the proposed technique is competitive with existing filter selection methods.
KeywordsFeature Selection Mutual Information Versus Versus Versus Versus Feature Selection Algorithm Versus Versus Versus Versus Versus
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