Using fuzzy undersampling and fuzzy PCA to improve imbalanced classification through Rotation Forest algorithm

Abstract

This paper proposed a novel undersampling method to reduce the imbalance ratio of a dataset using fuzzy memberships degrees as well as utilizing a new fuzzy principal components analysis (F-PCA) for the classification through Rotation Forest algorithm. In the undersampling phase, first two membership functions are defined on each feature (dimension); one… (More)

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