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.
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