Deep Reinforcement Learning for Multi-class Imbalanced Training

TL;DR

This paper presents a reinforcement learning framework tailored for multi-class imbalanced datasets, particularly in clinical contexts, demonstrating superior performance over existing methods in balancing class predictions.

Contribution

It introduces a novel RL-based approach with custom reward and training procedures for multi-class imbalanced data, extending existing RL architectures.

Findings

Outperforms state-of-the-art imbalanced learning methods

Achieves more fair and balanced classification

Improves minority class prediction significantly

Abstract

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.