Memetic Feature Selection Algorithm Based on Efficient Filter Local Search
Mohadeseh Montazeri, Hamid Reza Naji, Mitra Montazeri · Research Information System of Ardabil University of Medical Sciences (Ardabil University of Medical Sciences) · 2013
Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for classification purposes. It incorporates a filter method in wrapper method to improve classification performance and accelerates the search in identifying core feature subsets. Especially, this method deletes or adds a feature from a feature subset based on the multivariate feature information. Empirical study on commonly data sets of the University of California, Irvine shows that the proposed method outperforms existing methods. Furthermore, we have investigated several major issues of memetic algorithm to identify a good balance between local search and genetic search so as to maximize search quality in the hybrid filter and wrapper memetic algorithm.