Deep Reinforcement Learning for Multi-class Imbalanced Training. (arXiv:2205.12070v1 [cs.LG])

With the rapid growth of memory and computing power, datasets are becoming
increasingly complex and imbalanced. This is especially severe in the context
of clinical data, where there may be one rare event for many cases in the
majority class. We introduce an imbalanced classification framework, based on
reinforcement learning, for training extremely imbalanced data sets, and extend
it for use in multi-class settings. We combine dueling and double deep
Q-learning architectures, and formulate a custom reward function and
episode-training …

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