  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 procedure, specifically with the added capability of handling
multi-class imbalanced training. Using real-world clinical case studies, we
demonstrate that our proposed framework outperforms current state-of-the-art
imbalanced learning methods, achieving more fair and balanced classification,
while also significantly improving the prediction of minority classes.
