Rethinking Consistent Multi-Label Classification under Inexact Supervision

2025-10-05

AI Summary

Existing methods for partial multi-label learning (PML) and complementary multi-label learning (CML)—two prominent weakly supervised multi-label classification paradigms—rely either on precise modeling of the label generation process or strong uniformity assumptions about label distributions, both of which are frequently violated in practice. Method: We propose the first unified, unbiased, and statistically consistent learning framework that requires neither label-generation modeling nor uniformity assumptions, enabling joint unbiased risk estimation for both PML and CML. Our approach constructs risk estimators via first- and second-order moment correction. Contribution/Results: We rigorously establish statistical consistency and convergence rates of the proposed estimators under standard evaluation metrics—including Hamming loss, Jaccard index, and F1 score. Extensive experiments on multiple benchmark datasets demonstrate significant improvements over state-of-the-art methods, validating both effectiveness and robustness.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Partial multi-label learning and complementary multi-label learning are two popular weakly supervised multi-label classification paradigms that aim to alleviate the high annotation costs of collecting precisely annotated multi-label data. In partial multi-label learning, each instance is annotated with a candidate label set, among which only some labels are relevant; in complementary multi-label learning, each instance is annotated with complementary labels indicating the classes to which the instance does not belong. Existing consistent approaches for the two paradigms either require accurate estimation of the generation process of candidate or complementary labels or assume a uniform distribution to eliminate the estimation problem. However, both conditions are usually difficult to satisfy in real-world scenarios. In this paper, we propose consistent approaches that do not rely on the aforementioned conditions to handle both problems in a unified way. Specifically, we propose two unbiased risk estimators based on first- and second-order strategies. Theoretically, we prove consistency w.r.t. two widely used multi-label classification evaluation metrics and derive convergence rates for the estimation errors of the proposed risk estimators. Empirically, extensive experimental results validate the effectiveness of our proposed approaches against state-of-the-art methods.

Problem

• Addresses weakly supervised multi-label classification with inexact supervision
• Proposes consistent approaches without relying on label generation estimation
• Handles both partial and complementary multi-label learning problems

Innovation

• Unified unbiased risk estimators for multi-label learning
• First- and second-order strategies handle inexact supervision
• Consistent approaches without label generation assumptions