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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 9569161, 6 pages
http://dx.doi.org/10.1155/2016/9569161
Research Article

Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Computer Engineering Department, Dumlupinar University, 43000 Kütahya, Turkey

Received 6 November 2015; Accepted 1 February 2016

Academic Editor: Thunshun W. Liao

Copyright © 2016 Gürcan Yavuz and Doğan Aydin. 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.

Abstract

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets.