标签级别的偶然不确定性和认知不确定性量化
Label-wise Aleatoric and Epistemic Uncertainty Quantification
AI总结：
本文提出一种基于标签级分解的不确定性量化新方法，在单个类别层面量化分类任务中的总、偶然和认知不确定性，采用方差等非类别度量克服了基于熵方法的局限，并在多种基准数据集上验证了其有效性和理想性质。
摘要：
我们提出了一种基于不确定性度量的标签级分解的分类任务不确定性量化新方法。这种标签级视角允许在单个类别层面量化不确定性，从而改进成本敏感决策并帮助理解不确定性来源。此外，它允许在方差等非类别度量的基础上定义总不确定性、偶然不确定性和认知不确定性，超越了常见的基于熵的度量。特别是，基于方差的度量解决了文献中近期讨论的现有方法的一些局限性。我们证明所提出的度量满足诸多理想性质。通过对多种基准数据集（包括准确的不确定性量化至关重要的医学领域应用）的实证评估，我们验证了标签级不确定性量化的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
