Journal Article

Deep reinforcement learning for multi-class imbalanced training: applications in healthcare

Soltan, Andrew A. S., Lachapelle, Alexander S., Eyre, David W., Clifton, David A., Yang, Jenny, El-Bouri, Rasheed, O’Donoghue, Odhran, Lu, Lei

2024

Overview

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 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.

• Publisher: Springer US,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Case studies / Classification / Computer Science / Control / Datasets / Deep learning / Electronic health records / Machine Learning / Mechatronics / Methods / Natural Language Processing (NLP) / Robotics / Simulation and Modeling / Special Issue on Reinforcement Learning for Real Life