Overview

Ordinal regression studies prediction when labels have a meaningful order, such as disease stages, proficiency levels, or quality ratings, so confusing neighboring categories is usually less costly than making a distant error. Common approaches include cumulative-link and threshold-based models, ordinal-aware losses and soft-label distributions, and pairwise or contrastive methods that learn rank differences; newer work also incorporates hierarchy, multimodal features, and generative models. Applications span medical grading, age and image-quality estimation, speech assessment, and structured preference prediction, with evaluation often using mean absolute error, ranked probability scores, or quadratic weighted kappa alongside accuracy. A central challenge is that category order is known but the distances between levels and the uncertainty of labels often are not, making calibration, class imbalance, and transfer across datasets persistent concerns.

Latest papers

CardsList

38 papers2 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. A novel ordinal multi-view aggregation scheme for oak defoliation

    May 27, 2026Francisco Bérchez-Moreno, Ricardo Enrique Hernández-Lambraño, David Guijo-Rubio +4Ensemble LearningMulti-View Learning

  2. Ordinal Semantic Segmentation Applied to Medical and Odontological Images

    Mar 21, 2026Mariana Dória Prata Lima, Gilson Antonio Giraldi, Jaime S. CardosoSemantic SegmentationMedical Image Segmentation

  3. Classifier Pooling for Modern Ordinal Classification

    Mar 18, 2026Noam H. Rotenberg, Andreia V. Faria, Brian CaffoOrdinal Regression

  4. TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision

    Mar 12, 2026Robinson Umeike, Cuong Pham, Ryan Hausen +8Disaster Damage AssessmentBuilding Damage Assessment

  5. Directed Ordinal Diffusion Regularization for Progression-Aware Diabetic Retinopathy Grading

    Feb 25, 2026Huangwei Chen, Junhao Jia, Ruocheng Li +7Diabetic Retinopathy GradingOrdinal Regression

  6. An Effective Strategy for Modeling Score Ordinality and Non-uniform Intervals in Automated Speaking Assessment

    Aug 27, 2025Tien-Hong Lo, Szu-Yu Chen, Yao-Ting Sung +1Speech ProcessingOrdinal Regression