Feature selection with discrete binary differential evolution

The processing of data from the database using data mining algorithms need more special methods. In fact, some redundancy and irrelevant attributes reduce the performance of data mining, so the problem of feature subset selection becomes important in data mining domain. This paper presentes a new algorithm which is called discrete binary differential evolution (BDE) algorithm to select the best feature subsets. The relativity of attributes is evaluated based on the idea of mutual information. Experiments using the new feature selection method as a preprocessing step for SVM, C&R Tree and RBF network are done. We find that the method is very effective to improve the correct classification rate on some datasets and the BDE algorithm is useful for feature subset selection.