Label-wise Aleatoric and Epistemic Uncertainty Quantification
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910472016494592 |
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| author | Sale, Yusuf Hofman, Paul Löhr, Timo Wimmer, Lisa Nagler, Thomas Hüllermeier, Eyke |
| author_facet | Sale, Yusuf Hofman, Paul Löhr, Timo Wimmer, Lisa Nagler, Thomas Hüllermeier, Eyke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Label-wise Aleatoric and Epistemic Uncertainty Quantification Sale, Yusuf Hofman, Paul Löhr, Timo Wimmer, Lisa Nagler, Thomas Hüllermeier, Eyke Machine Learning 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. |
| title | Label-wise Aleatoric and Epistemic Uncertainty Quantification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.02354 |