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.
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© 2007 Springer Berlin Heidelberg
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Ponsa, D., López, A. (2007). Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_8
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DOI: https://doi.org/10.1007/978-3-540-72847-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72846-7
Online ISBN: 978-3-540-72847-4
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