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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 268063, 8 pages
Research Article

Feature Selection in Decision Systems: A Mean-Variance Approach

1School of Informatics, Linyi University, Linyi 276005, China
2Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 211189, China
3School of Science, Linyi University, Linyi 276005, China

Received 6 April 2013; Accepted 24 April 2013

Academic Editor: Guanghui Wen

Copyright © 2013 Chengdong Yang 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.


Uncertainty measure is an important implement for characterizing the degree of uncertainty. It has been extensively applied in pattern recognition and data clustering. Because of instability of traditional uncertainty measures, mean-variance measure (MVM) is utilized to perform feature selection, which could depress disturbances and noises effectively. Thereby, a novel evaluation function based on MVM is designed. The forward greedy search algorithm (FGSA) with the proposed evaluation function is exploited to perform feature selection. Experiment analysis shows the validity and effectiveness of MVM.