用强化学习解决部分多标签学习中的标签模糊问题，提升模型准确性和可解释性。
From Ambiguity to Action: A POMDP Perspective on Partial Multi-Label Ambiguity and Its Horizon-One Resolution
- 将标签消歧与特征选择建模为部分可观测马尔可夫决策过程
- 生成高质量伪标签并输出可解释的特征排序结果
- 理论证明误差可分解为伪标签质量与样本量影响，适合需要可解释性的场景
在部分多标签学习（PML）中，真实标签不可观测，导致标签消歧重要却困难。候选标签的模糊性可能将错误传播至下游任务，如特征工程。为此，本文将消歧与特征选择联合建模为部分可观测马尔可夫决策过程（POMDP），将PML风险最小化转化为期望回报最大化。第一阶段通过强化学习训练Transformer策略，生成高质量硬伪标签；第二阶段将特征选择建模为序列强化学习问题，逐步选取特征并输出可解释的全局排序。进一步提供了PML-POMDP对应关系的理论分析及超出风险界，该界将误差分解为伪标签质量项与样本量项。多个指标和数据集上的实验验证了该框架的优势。
原文摘要 · Abstract (English)
In partial multi-label learning (PML), the true labels are unobserved, which makes label disambiguation important but difficult. A key challenge is that ambiguous candidate labels can propagate errors into downstream tasks such as feature engineering. To solve this issue, we jointly model the disambiguation and feature selection tasks as Partially Observable Markov Decision Processes (POMDP) to turn PML risk minimization into expected-return maximization. Stage 1 trains a transformer policy via reinforcement learning to produce high-quality hard pseudo-labels; Stage 2 describes feature selection as a sequential reinforcement learning problem, selecting features step by step and outputting an interpretable global ranking. We further provide the theoretical analysis of PML-POMDP correspondence and the excess-risk bound that decompose the error into pseudo label quality term and sample size. Experiments in multiple metrics and data sets verify the advantages of the framework.
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