In data mining, feature selection is an important phase in the data preprocessing process. Finding a subset of features such that a classification model constructed just using this subset has superior prediction accuracy than a model constructed solely using the full set of features is the problem of feature selection. Two hybrid strategies for feature selection are presented here. In any case, the best features are chosen by combining current feature selection methods or developing new ones from scratch. Classification models based on five classifiers are then built using the reduced dataset. An area under the receiver operating characteristic (ROC) curve (AUC) performance metric was used to measure classification accuracy in this study. It has been demonstrated empirically that the proposed methods can increase the performance of existing feature selection methods.
Hybid, Features selecton, Image, Wrapper, PSO, GWO
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