Supervised feature selection by clustering using conditional mutual information-based distances
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http://dx.doi.org/10.1016/j.patcog.2009.12.013 |
Metadatos
Título
Supervised feature selection by clustering using conditional mutual information-based distancesFecha de publicación
2010Editor
ElsevierISSN
0031-3203Cita bibliográfica
Pattern Recognition, 43, 6, p. 2068-2081Tipo de documento
info:eu-repo/semantics/articlePalabras clave / Materias
Resumen
In this paper, a supervised feature selection approach is presented, which is based on metric applied on continuous and discrete data representations. This method builds a dissimilarity space using information theoretic ... [+]
In this paper, a supervised feature selection approach is presented, which is based on metric applied on continuous and discrete data representations. This method builds a dissimilarity space using information theoretic measures, in particular conditional mutual information between features with respect to a relevant variable that represents the class labels. Applying a hierarchical clustering, the algorithm searches for a compression of the information contained in the original set of features. The proposed technique is compared with other state of art methods also based on information measures. Eventually, several experiments are presented to show the effectiveness of the features selected from the point of view of classification accuracy. © 2010 Elsevier Ltd. All rights reserved. [-]
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