Second-Order Uncertainty Quantification: Variance-Based Measures

• URL: http://arxiv.org/abs/2401.00276v1
• Date: Sat, 30 Dec 2023 16:30:52 GMT
• Title: Second-Order Uncertainty Quantification: Variance-Based Measures
• Authors: Yusuf Sale, Paul Hofman, Lisa Wimmer, Eyke H\"ullermeier, Thomas Nagler
• Abstract summary: This paper proposes a novel way to use variance-based measures to quantify uncertainty on the basis of second-order distributions in classification problems. A distinctive feature of the measures is the ability to reason about uncertainties on a class-based level, which is useful in situations where nuanced decision-making is required.
• Score: 2.3999111269325266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications. This paper proposes a novel way to use variance-based measures to quantify uncertainty on the basis of second-order distributions in classification problems. A distinctive feature of the measures is the ability to reason about uncertainties on a class-based level, which is useful in situations where nuanced decision-making is required. Recalling some properties from the literature, we highlight that the variance-based measures satisfy important (axiomatic) properties. In addition to this axiomatic approach, we present empirical results showing the measures to be effective and competitive to commonly used entropy-based measures.

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