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A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers
Kybernetika, Tome 34 (1998) no. 4, pp. 429-434 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Needs of feature selection in medium and large problems increases in many fields including medical and image processing fields. Previous comparative studies of feature selection algorithms are not satisfactory in problem size and in criterion function. In addition, no way has not shown to compare algorithms with different objectives. In this study, we propose a unified way to compare a large variety of algorithms. Our results show that the sequential floating algorithms promises for up to medium problems and genetic algorithms for medium and large problems.
Needs of feature selection in medium and large problems increases in many fields including medical and image processing fields. Previous comparative studies of feature selection algorithms are not satisfactory in problem size and in criterion function. In addition, no way has not shown to compare algorithms with different objectives. In this study, we propose a unified way to compare a large variety of algorithms. Our results show that the sequential floating algorithms promises for up to medium problems and genetic algorithms for medium and large problems.
Classification : 68T10, 68U99
Keywords: feature selection; pattern classifiers 
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     title = {A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers},
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Kudo, Mineichi; Sklansky, Jack. A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers. Kybernetika, Tome 34 (1998) no. 4, pp. 429-434. http://geodesic.mathdoc.fr/item/KYB_1998_34_4_a12/

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