用强化学习选关键样本，提升低资源医疗数据的迁移学习效果
RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
- 用强化学习自动挑选对迁移最有帮助的样本
- 在极端数据稀缺和类别不平衡下仍保持高模型性能
- 适合医疗等小样本、不均衡数据场景的模型优化
迁移学习中常用少样本微调策略，但其效果高度依赖样本选择质量。传统主动学习方法如不确定性采样和多样性采样在极低资源和类别严重不均衡条件下，常偏好异常值而非真正有信息量的样本，导致性能下降。本文提出RADS（强化自适应域采样），一种基于强化学习的稳健样本选择策略，用于识别最具信息量的样本。在多个真实临床数据集上的实验表明，该策略显著提升了模型的迁移能力，并在极端类别不平衡情况下保持了鲁棒性能，优于传统方法。
原文摘要 · Abstract (English)
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.
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