Least-Ambiguous Multi-Label Classifier
This addresses the challenge of costly full label annotations in multi-label learning for researchers and practitioners, though it is incremental as it builds on existing conformal prediction methods.
The paper tackles the problem of single-positive multi-label learning, where only one positive label is annotated per training instance despite multiple relevant labels, by proposing a model-agnostic approach using conformal prediction to produce calibrated set-valued outputs, resulting in consistent improvements on 12 benchmark datasets.
Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated per training instance, despite the presence of multiple relevant labels. This setting, known as single-positive multi-label learning (SPMLL), presents a significant challenge due to its extreme form of partial supervision. We propose a model-agnostic approach to SPMLL that draws on conformal prediction to produce calibrated set-valued outputs, enabling reliable multi-label predictions at test time. Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions. We evaluate our approach on 12 benchmark datasets, demonstrating consistent improvements over existing baselines and practical applicability.