Feature selection based on fusing mutual information and cross-validation

Wei-wei Li; Chun-ping Liu; Ning-qiang Chen

doi:10.1117/12.833134

30 October 2009 Feature selection based on fusing mutual information and cross-validation

Wei-wei Li, Chun-ping Liu, Ning-qiang Chen

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961W (2009) https://doi.org/10.1117/12.833134
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Wei-wei Li, Chun-ping Liu, and Ning-qiang Chen "Feature selection based on fusing mutual information and cross-validation", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961W (30 October 2009); https://doi.org/10.1117/12.833134

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