In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classifiers in this weakly supervised setting. While state-of-the-art methods already feature good predictive performance, they often suffer from miscalibrated uncertainty estimates. However, having well-calibrated uncertainty estimates is important, especially in safety-critical domains like medicine and autonomous driving. In this article, we propose a novel nearest-neighbor-based partial-label-learning algorithm that leverages Dempster-Shafer theory. Extensive experiments on artificial and real-world datasets show that the proposed method provides a well-calibrated uncertainty estimate and achieves competitive prediction performance. Additionally, we prove that our algorithm is risk-consistent.

翻译：在现实应用中，常会遇到标注模糊的数据，即不同标注者分配了相互冲突的类别标签。部分标签学习允许在这种弱监督场景下训练分类器。尽管现有方法已具备良好的预测性能，但它们往往存在不确定性估计校准不佳的问题。然而，在医学和自动驾驶等安全关键领域，获得校准良好的不确定性估计至关重要。本文提出了一种基于最近邻的部分标签学习算法，该算法利用登普斯特-谢弗理论。在人工数据集和真实数据集上的大量实验表明，所提方法提供了校准良好的不确定性估计，并实现了具有竞争力的预测性能。此外，我们证明了该算法具有风险一致性。