Filter-Type Variable Selection Based on Information Measures for Regression Tasks
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Otros documentos de la autoría: Latorre Carmona, Pedro; Martínez Sotoca, José; Pla, Filiberto
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadatos
Título
Filter-Type Variable Selection Based on Information Measures for Regression TasksFecha de publicación
2012Editor
MDPI AGISSN
1099-4300Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.mdpi.com/1099-4300/14/2/323Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Entropy, 2012, 14(2)Derechos de acceso
info:eu-repo/semantics/openAccess
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