Feature selection based on fusing mutual information and cross-validation
Weiwei Li, Chunping Liu, Ning-qiang Chen, Zhaohui Wang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.