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

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

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Received: 8 December 2011; in revised form: 9 February 2012 / Accepted: 12 February 2012 / Published: 17 February 2012
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Abstract: 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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA Style

Latorre Carmona P, Sotoca JM, Pla F. Filter-Type Variable Selection Based on Information Measures for Regression Tasks. Entropy. 2012; 14(2):323-343.

Chicago/Turabian Style

Latorre Carmona, Pedro; Sotoca, José Martínez; Pla, Filiberto. 2012. "Filter-Type Variable Selection Based on Information Measures for Regression Tasks." Entropy 14, no. 2: 323-343.


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