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In this paper, we propose a novel weighted combination feature selectionmethod using bootstrap and fuzzy sets. The proposed method mainly consists ofthree processes, including fuzzy sets generation using bootstrap, weightedcombination of fuzzy sets and feature ranking based on defuzzification. Weimplemented the proposed method by combining four state-of-the-art featureselection methods and evaluated the performance based on three publiclyavailable biomedical datasets using five-fold cross validation. Based on thefeature selection results, our proposed method produced comparable (if notbetter) classification accuracies to the best of the individual featureselection methods for all evaluated datasets. More importantly, we also appliedstandard deviation and Pearson's correlation to measure the stability of themethods. Remarkably, our combination method achieved significantly higherstability than the four individual methods when variations and size reductionswere introduced to the datasets.

A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets