Label-wise Aleatoric and Epistemic Uncertainty Quantification

• URL: http://arxiv.org/abs/2406.02354v1
• Date: Tue, 4 Jun 2024 14:33:23 GMT
• Title: Label-wise Aleatoric and Epistemic Uncertainty Quantification
• Authors: Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier,
• Abstract summary: We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. We show that our proposed measures adhere to a number of desirable properties.
• Score: 15.642370299038488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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