MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Zhining Liu
Zhining Liu
Pengfei Wei
Pengfei Wei
Wei Cao
Wei Cao

NeurIPS, 2020.

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Other Links: arxiv.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where thei...More

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