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The Scientific World Journal
Volume 2014, Article ID 972125, 8 pages
http://dx.doi.org/10.1155/2014/972125
Research Article

An Improved Feature Selection Based on Effective Range for Classification

1College of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China
2National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130000, China
3Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130000, China
4School of Mathematics and Statistics, Northeast Normal University, Changchun 130000, China

Received 29 August 2013; Accepted 2 December 2013; Published 4 February 2014

Academic Editors: C.-C. Chang and J. Shu

Copyright © 2014 Jianzhong Wang 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 is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selection (ERGS) is proposed. However, ERGS only considers the overlapping area (OA) among effective ranges of each class for every feature; it fails to handle the problem of the inclusion relation of effective ranges. In order to overcome this limitation, a novel efficient statistical feature selection approach called improved feature selection based on effective range (IFSER) is proposed in this paper. In IFSER, an including area (IA) is introduced to characterize the inclusion relation of effective ranges. Moreover, the samples’ proportion for each feature of every class in both OA and IA is also taken into consideration. Therefore, IFSER outperforms the original ERGS and some other state-of-the-art algorithms. Experiments on several well-known databases are performed to demonstrate the effectiveness of the proposed method.