让只标注一个标签的数据，也能准确预测多个真实标签。
Least-Ambiguous Multi-Label Classifier
- 用置信度校准方法生成可靠标签集合，无需假设标签分布。
- 在12个数据集上均优于现有方法，提升多标签预测精度。
- 适合标注成本高、只给单标签的场景，如医学图像分析。
多标签学习通常需要为训练样本标注所有相关标签，但完整标注代价高昂且耗时。许多数据集中每条样本仅标注一个正标签，尽管存在多个真实相关标签。这种设定称为单正标签多标签学习（SPMLL），因极端部分监督而极具挑战性。本文提出一种模型无关的方法，基于置信区间预测生成校准的集合输出，可在测试时实现可靠的多标签预测。该方法在不依赖标签分布假设的前提下，弥合了单标签训练与多标签评估之间的监督差距。我们在12个基准数据集上进行了评估，结果表明该方法持续优于现有基线，具备实际应用价值。
原文摘要 · Abstract (English)
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.
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