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A Fuzzy Aggregation based Ensemble Framework for Accurate and Stable Feature Selection

A novel ensemble feature selection (FS) framework using fuzzy aggregation is proposed in this paper. It consists of three main steps: distribution generation of feature importance, distribution ensemble using fuzzy aggregation, and defuzzification for feature ranking. Based on four state-of-the-art FS methods (named as base selectors in our algorithm) selected from different method categories, different fuzzy aggregation operators were implemented to achieve ensemble learning for decision making. A training data repository that consists of eight datasets was used for parameter tuning of the proposed framework. The proposed framework using drastic sum aggregation achieved the best performance and was subsequently evaluated on eight independent testing datasets. Remarkably, the proposed method achieved the best classification accuracy and the highest stability compared with the four base FS methods. It also outperformed our previously proposed score based ensemble method [1].