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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
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.
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: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.
arXiv:2607. 21003v1 Announce Type: new Abstract: Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order.
arXiv:2112. 02353v3 Announce Type: replace-cross Abstract: Hierarchical classification aims to sort the object into a hierarchical structure of categories.
arXiv:2607. 23575v1 Announce Type: cross Abstract: Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered.