RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
This work addresses sample selection challenges in transfer learning for clinical applications, particularly under low-resource and imbalanced conditions, representing an incremental improvement over existing active learning methods.
The paper tackles the problem of selecting informative samples for few-shot fine-tuning in low-resource and class-imbalanced clinical settings, where traditional active learning methods often fail. It introduces RADS, a reinforcement learning-based sample selection strategy, which improves model transferability and maintains robust performance under extreme class imbalance compared to traditional methods.
A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examples. Active learning methods such as uncertainty sampling and diversity sampling can select useful samples. However, under extremely low-resource and class-imbalanced conditions, they often favor outliers rather than truly informative samples, resulting in degraded performance. In this paper, we introduce RADS (Reinforcement Adaptive Domain Sampling), a robust sample selection strategy using reinforcement learning (RL) to identify the most informative samples. Experimental evaluations on several real world clinical datasets show our sample selection strategy enhances model transferability while maintaining robust performance under extreme class imbalance compared to traditional methods.