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Entropy 2012, 14(2), 323-343; doi:10.3390/e14020323

Filter-Type Variable Selection Based on Information Measures for Regression Tasks

Institute of New Imaging Technologies, Universidad Jaume I, Campus del Riu Sec, s/n, 12071 Castellón de la Plana, Spain
Author to whom correspondence should be addressed.
Received: 8 December 2011 / Revised: 9 February 2012 / Accepted: 12 February 2012 / Published: 17 February 2012
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This paper presents a supervised variable selection method applied to regression problems. This method selects the variables applying a hierarchical clustering strategy based on information measures. The proposed technique can be applied to single-output regression datasets, and it is extendable to multi-output datasets. For single-output datasets, the method is compared against three other variable selection methods for regression on four datasets. In the multi-output case, it is compared against other state-of-the-art method and tested using two regression datasets. Two different figures of merit are used (for the single and multi-output cases) in order to analyze and compare the performance of the proposed method.
Keywords: variable selection; conditional mutual information variable selection; conditional mutual information

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Latorre Carmona, P.; Sotoca, J.M.; Pla, F. Filter-Type Variable Selection Based on Information Measures for Regression Tasks. Entropy 2012, 14, 323-343.

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