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arXiv AI By Mohamed Karim Belaid, Maximilian Rabus, Eyke H\"ullermeier

Uncertainty Quantification in Pairwise Difference Learning for Classification

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The paper introduces a pairwise difference learning (PDL) approach for classification, where a model predicts the outcome difference between two instances and uses these predictions to classify a query instance by averaging over training examples. It presents a meta‑learning technique tailored to this setting and tackles uncertainty quantification by decomposing total uncertainty into aleatoric and epistemic components, extending these measures to PDL. Extensive empirical studies evaluate both predictive accuracy and the model’s awareness of uncertainty.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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