Imbalanced learning(IL),i.e.,learning unbiased models from class-imbalanced data, is a challenging problem.

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 their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics.

The authors propose to address this issue by using a meta/ensemble-learning framework. In this framework, the meta-algorithm deduces an appropriate data sampling strategy that generates a data set for a new base learner to train. The meta-learner is trained using reinforcement learning. The meta-state is composed of two histograms that are respectively the empirical distributions of the training and validation error. The meta-sampler uses this state to sample a coefficient.

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 their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model.