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[2510.04091] Rethinking Consistent Multi-Label Classification Under Inexact Supervision
Summary
This paper presents a novel approach to multi-label classification under inexact supervision, addressing the limitations of existing methods that rely on accurate label estimation.
Why It Matters
The research tackles the challenge of high annotation costs in multi-label classification, offering consistent methods that improve performance without needing precise label generation. This is crucial for real-world applications where data labeling can be expensive and time-consuming.
Key Takeaways
- Introduces consistent approaches for multi-label classification that do not depend on accurate label estimation.
- Proposes two risk estimators based on first- and second-order strategies.
- Demonstrates theoretical consistency with common multi-label evaluation metrics.
- Presents empirical results showing effectiveness against state-of-the-art methods.
- Addresses the practical challenges of inexact supervision in real-world datasets.
Computer Science > Machine Learning arXiv:2510.04091 (cs) [Submitted on 5 Oct 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:Rethinking Consistent Multi-Label Classification Under Inexact Supervision Authors:Wei Wang, Tianhao Ma, Ming-Kun Xie, Gang Niu, Masashi Sugiyama View a PDF of the paper titled Rethinking Consistent Multi-Label Classification Under Inexact Supervision, by Wei Wang and 4 other authors View PDF HTML (experimental) 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 risk estimators based on first- and s...
