Label-wise Aleatoric and Epistemic Uncertainty Quantification
<|reference_start|>Label-wise Aleatoric and Epistemic Uncertainty Quantification: 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.<|reference_end|>
@article{sale2024label-wise,
    title={Label-wise Aleatoric and Epistemic Uncertainty Quantification},
    author={Yusuf Sale, Paul Hofman, Timo L"ohr, Lisa Wimmer, Thomas Nagler, Eyke
  H"ullermeier},
    journal={arXiv preprint arXiv:2406.02354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02354},
    primaryClass={cs.LG stat.ML}
}