Uncertainty-Aware Partial-Label Learning
Tobias Fuchs Florian Kalinke Klemens Böhm
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.