A comparative evaluation of medium - and large - scale featureselectors for pattern classi

Needs of feature selection in medium and large problems (medium-and large-scale feature selection) increases in many elds including medical and image processing elds. Previous comparative studies of feature selection algorithms are not suucient in problem size and in criterion function. In addition, no way has not shown to compare algorithms with diierent objectives. In this study, we propose a uniied way to compare a large variety of algorithms. Our results on some medium and large problems show that the sequential oating algorithms promises for up to medium problems and genetic algorithms for medium and large problems.