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.

针对日益复杂和不平衡的数据集，提出了一种基于强化学习的不平衡分类框架，并将其扩展为多类设置。在真实的临床案例研究中，实验表明该框架优于现有的不平衡学习方法，可以实现更公平和平衡的分类，同时也显着提高少数类别的预测准确性。

多类别不平衡训练的深度强化学习