Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction

• URL: http://arxiv.org/abs/2507.00733v1
• Date: Tue, 01 Jul 2025 13:31:58 GMT
• Title: Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction
• Authors: Stefan Haas, Eyke Hüllermeier,
• Abstract summary: Ordinal classification problems, where labels exhibit a natural order, are prevalent in high-stakes fields such as medicine and finance.<n>We introduce a novel class of measures of aleatoric and epistemic uncertainty in ordinal classification.<n>These measures effectively capture the trade-off in ordinal classification between exact hit-rate and minimial error distances.
• Score: 21.602569813024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ordinal classification problems, where labels exhibit a natural order, are prevalent in high-stakes fields such as medicine and finance. Accurate uncertainty quantification, including the decomposition into aleatoric (inherent variability) and epistemic (lack of knowledge) components, is crucial for reliable decision-making. However, existing research has primarily focused on nominal classification and regression. In this paper, we introduce a novel class of measures of aleatoric and epistemic uncertainty in ordinal classification, which is based on a suitable reduction to (entropy- and variance-based) measures for the binary case. These measures effectively capture the trade-off in ordinal classification between exact hit-rate and minimial error distances. We demonstrate the effectiveness of our approach on various tabular ordinal benchmark datasets using ensembles of gradient-boosted trees and multi-layer perceptrons for approximate Bayesian inference. Our method significantly outperforms standard and label-wise entropy and variance-based measures in error detection, as indicated by misclassification rates and mean absolute error. Additionally, the ordinal measures show competitive performance in out-of-distribution (OOD) detection. Our findings highlight the importance of considering the ordinal nature of classification problems when assessing uncertainty.

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