arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.
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arXiv:2606. 25769v1 Announce Type: new Abstract: In many prediction problems in medical applications, target labels exhibit an inherent ordinal structure, where class ordering reflects clinically meaningful severity levels.
The paper introduces the Adaptive Margin Ordinal Loss (AMOL), a new loss function designed to reduce the tendency of neural networks to predict center classes in ordinal classification tasks—a problem called center‑class hedging. AMOL applies a multiplicative weight to per‑class loss terms that is large only when a candidate class is near the center while the true label is far from it, thereby discouraging hedging. The authors also propose the Center‑Hedging Rate (CHR) metric to quantify this failure mode and demonstrate that AMOL achieves state‑of‑the‑art Quadratic Weighted Kappa scores on four benchmarks, with an asymmetric variant eliminating hedging on the Abalone dataset.
arXiv:2606. 24959v1 Announce Type: new Abstract: Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity of ordinal errors.
arXiv:2606. 17858v1 Announce Type: new Abstract: Machine Learning (ML) techniques have been applied to various problems.
arXiv:2211. 11278v3 Announce Type: replace-cross Abstract: Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry.
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.