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Deep Reinforcement Learning for Multi-class Imbalanced Training

2022-05-24 · Jenny Yang, Rasheed el-Bouri, Odhran O'Donoghue, Alexander S. Lachapelle, Andrew A. S. Soltan, David A. Clifton

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.

📄 PDF Abstract BibTeX arXiv:2205.12070

Code (1)

yangjenny/ImbalancedLearningRL 공식 구현 tf

Tasks

Deep Reinforcement Learningimbalanced classificationQ-Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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2022-05-26 · *SEM (NAACL) 2022 7 · Yuexin Wu, Xiaolei Huang

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2024-07-15 · Leon Eshuijs, Shihan Wang, Antske Fokkens

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