arXiv:2608. 04652v1 Announce Type: cross Abstract: Image mixup is a widely adopted data augmentation strategy, yet it is ill-suited for ordinal classification tasks such as medical disease grading, where labels encode a progression of severity.
By Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting Chen
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.
By Manisha Kandel
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.
By Stefan Haas, Luca Killmaier, Alireza Javanmardi, Eyke H\"ullermeier
GRIN+ is a new machine unlearning framework that targets fast and precise data erasure in imbalanced medical datasets. It separates unlearning‑specific knowledge from general representations by analyzing gradient contributions of forget and retain sets, introduces a class‑adaptive influence scoring to counter gradient dominance, and uses a direction‑constrained update to protect essential clinical knowledge. Benchmarks on skin cancer, brain tumor, and breast ultrasound data show that GRIN+ balances privacy, efficiency, and utility, achieving high diagnostic accuracy and faster runtime than existing methods.
By Minghui Huang, Junxiao Wang
arXiv:2607. 16802v1 Announce Type: new Abstract: Deep survival models are evaluated almost exclusively by the concordance index (C-index), yet they are commonly trained using likelihood objectives such as the Cox partial likelihood, discrete-time negative log-likelihood, and DeepHit likelihood.
By Meixu Chen, Kai Wang, Jing Wang
arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.
By Rafael Ayll\'on-Gavil\'an, Francisco Jos\'e Mart\'inez-Estudillo, David Guijo-Rubio, C\'esar Herv\'as-Mart\'inez, Pedro A. Guti\'errez