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

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, base...

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Bibliographic Details
Published in:Machine learning 2024-05, Vol.113 (5), p.2655-2674
Main Authors: Yang, Jenny, El-Bouri, Rasheed, O’Donoghue, Odhran, Lachapelle, Alexander S., Soltan, Andrew A. S., Eyre, David W., Lu, Lei, Clifton, David A.
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-023-06481-z