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Mathematical Problems in Engineering
Volume 2013, Article ID 241517, 8 pages
Research Article

Application of Global Optimization Methods for Feature Selection and Machine Learning

1College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
2College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China

Received 2 September 2013; Revised 12 October 2013; Accepted 14 October 2013

Academic Editor: Gelan Yang

Copyright © 2013 Shaohua Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. The process reduces the number of features by removing irrelevant and redundant data. This paper proposed a novel immune clonal genetic algorithm based on immune clonal algorithm designed to solve the feature selection problem. The proposed algorithm has more exploration and exploitation abilities due to the clonal selection theory, and each antibody in the search space specifies a subset of the possible features. Experimental results show that the proposed algorithm simplifies the feature selection process effectively and obtains higher classification accuracy than other feature selection algorithms.