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Proceedings Paper

Feature selection based on fusing mutual information and cross-validation
Author(s): Wei-wei Li; Chun-ping Liu; Ning-qiang Chen
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Paper Abstract

Many algorithms have been proposed in literature for feature selection; unfortunately, none of them ensures a perfect result. Here we propose an adaptive sequential floating forward feature selection algorithm which achieves accuracy results higher than that of already existing algorithms and naturally adaptive for implementation into the number of best feature subset to be selected. The basic idea of the proposed algorithm is to adopt two relatively well-settled algorithms for the problem at hand and combine mutual information and Cross-Validation through suitable fusion techniques, with the aim of taking advantage of the adopted algorithms' capabilities, at the same time, limiting their deficiencies. This method adaptively obtains the number of features to be selected according to dimensions of original feature set, and Dempster-Shafer Evidential Theory is used to fuse Max-Relevance, Min-Redundancy and CVFS. Extensive experiments show that the higher accuracy of classification and the less redundancy of features could be achieved.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961W (30 October 2009); doi: 10.1117/12.833134
Show Author Affiliations
Wei-wei Li, Soochow Univ. (China)
Chun-ping Liu, Soochow Univ. (China)
Ning-qiang Chen, Suzhou Unimap Software Co. Ltd. (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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