Ordinal-Aware Calibration for Ordinal Classification

Excerpt

arXiv:2410.15658v4 Announce Type: replace Abstract: Deep neural networks frequently produce overconfident, miscalibrated predictions. In ordinal classification, predictions must also adhere to a unimodal and order-consistent structure, a requirement that has dominated prior work while overlooking calibration. We formalize this joint challenge as ordinal calibration for the first time and propose the Ordinal loss for Calibration and Unimodality (ORCU). Unlike incremental modular combinations, ORC