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.

在真实应用中经常遇到标签模糊的数据，其中不同的标注者会给出冲突的类别标签。部分标签学习允许在这种弱监督的环境下训练分类器。虽然最先进的方法已经具有良好的预测性能，但它们经常受到错误校准的不确定性估计的影响。然而，拥有良好校准的不确定性估计在医学和自动驾驶等关键领域中尤为重要。本文中，我们提出了一种基于邻近 - 邻居的部分标签学习算法，利用了 Dempster-Shafer 理论。对人工和真实数据集的大量实验证明，所提出的方法提供了良好校准的不确定性估计，并取得了有竞争力的预测性能。此外，我们证明了我们的算法是风险一致的。