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- Title
Filter-Type Variable Selection Based on Information Measures for Regression Tasks.
- Authors
Latorre Carmona, Pedro; Martíinez Sotoca, José; Pla, Filiberto
- 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.
- Subjects
INFORMATION technology; REGRESSION analysis; CLUSTER analysis (Statistics); MATHEMATICAL variables; ACQUISITION of data; MATHEMATICAL analysis
- Publication
Entropy, 2012, Vol 14, Issue 2, p323
- ISSN
1099-4300
- Publication type
Article
- DOI
10.3390/e14020323