Feature Selection in Decision Systems: A Mean-Variance Approach. (26th May 2013)

• Record Type: Journal Article
• Authors: Yang, Chengdong; Zhang, Wenyin; Zou, Jilin; Hu, Shunbo; Qiu, Jianlong
• Other Names: Wen Guanghui Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-05-26
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/268063
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.