From Ambiguity to Action: A POMDP Perspective on Partial Multi-Label Ambiguity and Its Horizon-One Resolution

TL;DR

This paper introduces a novel POMDP-based framework for partial multi-label learning, jointly addressing label disambiguation and feature selection through reinforcement learning, leading to improved accuracy and interpretability.

Contribution

It proposes a new POMDP formulation for PML, with a two-stage reinforcement learning approach for label disambiguation and feature selection, supported by theoretical analysis.

Findings

Enhanced label disambiguation accuracy

Improved feature selection interpretability

Superior performance across multiple datasets

Abstract

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.