用强化学习动态调整采样策略，提升回归主动学习的效率与精度
Adaptive Active Learning for Regression via Reinforcement Learning
- 将权重选择建模为强化学习问题，实现自适应采样
- 在18个基准数据集上，准确率和标注效率均优于基线方法
- 特别适合数据分布不规则、密集区域高误差的场景
回归任务中的主动学习通过选择最具信息量的样本降低标注成本。Improved Greedy Sampling（iGS）是一种主流方法，通过静态乘法规则平衡特征空间多样性与输出空间不确定性。本文提出加权改进贪婪采样（WiGS），以动态加法准则替代该框架。将权重选择建模为强化学习问题，使智能体在学习过程中自适应调节探索与考察的平衡。在18个基准数据集及一个合成环境上的实验表明，WiGS在准确率和标注效率方面均优于iGS及其他基线方法，尤其在数据密度不规则的领域表现突出，此时基线的乘法规则会忽略密集区域中的高误差样本。
原文摘要 · Abstract (English)
Active learning for regression reduces labeling costs by selecting the most informative samples. Improved Greedy Sampling is a prominent method that balances feature-space diversity and output-space uncertainty using a static, multiplicative rule. We propose Weighted improved Greedy Sampling (WiGS), which replaces this framework with a dynamic, additive criterion. We formulate weight selection as a reinforcement learning problem, enabling an agent to adapt the exploration-investigation balance throughout learning. Experiments on 18 benchmark datasets and a synthetic environment show WiGS outperforms iGS and other baseline methods in both accuracy and labeling efficiency, particularly in domains with irregular data density where the baseline's multiplicative rule ignores high-error samples in dense regions.
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