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This paper investigates deep clustering methods on imbalanced tabular data and proposes two novel ensemble approaches that aggregate clustering assignments across embedding dimensions or via majority voting, outperforming individual methods on 16 datasets.
Proposes a tree-based ensemble classifier called KFDA Forest that applies kernel Fisher discriminant analysis for rotation, using bootstrap and random variable subsets to promote diversity, achieving improved classification accuracy.