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The paper introduces RADS, a reinforcement learning-based sample selection strategy for few-shot transfer learning in low-resource and imbalanced clinical settings. RADS uses RL to adaptively select informative samples, mitigating the tendency of traditional active learning methods to favor outliers under such conditions. Experiments on real-world clinical datasets demonstrate that RADS enhances model transferability and maintains robust performance compared to standard approaches.
RL-based sample selection beats traditional active learning for transfer learning when data is scarce and imbalanced, especially in clinical settings.
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.
