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Computational Intelligence and Neuroscience
Volume 2014, Article ID 479289, 10 pages
http://dx.doi.org/10.1155/2014/479289
Research Article

Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 15 May 2014; Revised 27 July 2014; Accepted 10 August 2014; Published 25 August 2014

Academic Editor: Saeid Sanei

Copyright © 2014 Kai Zeng 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.

Abstract

Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals. To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy. Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game. The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures. Finally, the proposed method is tested on several UCI datasets. The results show that neighborhood entropy-based cooperative game theory model (NECGT) yield better performance than classical ones.