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arXiv · 2406.02354

Label-wise Aleatoric and Epistemic Uncertainty Quantification

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

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Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier. 2024-06-04. Label-wise Aleatoric and Epistemic Uncertainty Quantification. https://arxiv.org/abs/2406.02354

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