General framework for class-specific feature selection

Authors:
Bárbara B. Pineda-Bautista;J. A. Carrasco-Ochoa;J. Fco. Martınez-Trinidad
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1 Sta Marıa Tonanzintla, Puebla, CP 72840, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1 Sta Marıa Tonanzintla, Puebla, CP 72840, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1 Sta Marıa Tonanzintla, Puebla, CP 72840, Mexico
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Commonly, when a feature selection algorithm is applied, a single feature subset is selected for all the classes, but this subset could be inadequate for some classes. Class-specific feature selection allows selecting a possible different feature subset for each class. However, all the class-specific feature selection algorithms have been proposed for a particular classifier, which reduce their applicability. In this paper, a general framework for using any traditional feature selector for doing class-specific feature selection, which allows using any classifier, is proposed. Experimental results and a comparison against traditional feature selectors showing the suitability of the proposed framework are included.