Guide to MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Class Imbalance is quite a known problem in datasets, and Imbalanced Learning is used to handle this problem by learning an unbiased model in the data.  Existing approaches include Resampling, Reweighting, Ensemble Methods and Meta-Learning Methods. In this article, we will discuss one new method that has outperformed many of the previous methods. It combines both ensemble methods and meta-learning methods called MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler. This method was presented and submitted at Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) by Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang and this is a collaborative project of Jilin University, National University of Singapore, University of Technology Sydn