Label-wise Aleatoric and Epistemic Uncertainty Quantification

Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier

Keywords: uncertainty quantification, label-wise uncertainty, second-order distribution, variance

TL;DR: We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures.

Abstract: We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improving cost-sensitive decision-making and helping understand the sources of uncertainty. Furthermore, it allows to define total, aleatoric, and epistemic uncertainty on the basis of non-categorical measures such as variance, going beyond common entropy-based measures. In particular, variance-based measures address some of the limitations associated with established methods that have recently been discussed in the literature. We show that our proposed measures adhere to a number of desirable properties. Through empirical evaluation on a variety of benchmark data sets -- including applications in the medical domain where accurate uncertainty quantification is crucial -- we establish the effectiveness of label-wise uncertainty quantification.

List Of Authors: Sale, Yusuf and Hofman, Paul and L\''ohr, Timo and Wimmer, Lisa and Nagler, Thomas and H\''ullermeier, Eyke

Code Url: https://github.com/YSale/label-uq

Submission Number: 686