A. Azari

MILES++: A Generalizable Clustering-Based Ensemble Framework for Multiclass Imbalanced Learning

Imbalanced multiclass learning remains challenging due to skewed class distributions, class overlap, and heterogeneous within-class structure. We revisit the Multiclass Imbalance Learning in Ensembles through Selective Sampling (MILES) framework and study two clustering-based variants: MILES \({}^{k}\) , which uses \(k...