Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion

Abstract

This paper proposes an incremental method for feature selection, aimed at identifying attributes in a dataset that allow to buid good classifiers at low computational cost. The basis of the approach is the minimal-redundancy-maximal-relevance (mRMR) framework, which attempts to select features relevant for a given classification task, avoiding redundancy among them. Relevance and redundancy have been popularly defined in terms of information theory concepts. In this paper a modification of the mRMR framework is proposed, based on a more proper quantification of the redundancy among features. Experimental work on discrete–valued datasets shows that classifiers built using features selected by the proposed method are more accurate than the ones obtained using original mRMR features.