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

Authors:
Daniel Ponsa;Antonio López
Affiliations:
Centre de Visió per Computador, Universitat Autònoma de Barcelona, Edifici O, 08193 Bellaterra, Barcelona, Spain;Centre de Visió per Computador, Universitat Autònoma de Barcelona, Edifici O, 08193 Bellaterra, Barcelona, Spain
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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Abstract

This paper proposes an incremental method for feature selection, aimed at identifying attributes in a dataset that allow to buid goodclassifiers 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.