DocumentCode :
639754
Title :
A novel memetic feature selection algorithm
Author :
Montazeri, Mina ; Naji, Hamid Reza ; Montazeri, Mina ; Faraahi, Ahmad
Author_Institution :
Dept. of Comput. Eng. & Inf. Technol., Payame Noor Univ., Tehran, Iran
fYear :
2013
fDate :
28-30 May 2013
Firstpage :
295
Lastpage :
300
Abstract :
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 feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate 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.
Keywords :
computational complexity; genetic algorithms; pattern classification; search problems; NP-hard problem; classification problem; feature subset; filter method; genetic algorithm; heuristic algorithms; memetic feature selection algorithm; multivariate feature information; search strategies; Accuracy; Algorithm design and analysis; Biological cells; Classification algorithms; Filtering algorithms; Memetics; Search problems; Feature Selection; Local search; Memetic Algorithms; Meta-Heuristic Algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Knowledge Technology (IKT), 2013 5th Conference on
Conference_Location :
Shiraz
Print_ISBN :
978-1-4673-6489-8
Type :
conf
DOI :
10.1109/IKT.2013.6620082
Filename :
6620082
Link To Document :
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