Exploiting Structured Global and Neighbor Orders for Enhanced Ordinal Regression

Ordinal regression combines classification and regression techniques, constrained by the intrinsic order among categories. It has wide-ranging applications in real-world scenarios, such as product quality grading, medical diagnoses, and facial age recognition, where understanding ranked relationships is crucial. Existing models, which often employ a series of binary classifiers with ordinal consistency loss, effectively enforce global order consistency but frequently encounter misclassification errors between adjacent categories. Achieving both global and local (neighbor-level) ordinal consistency, however, remains a significant challenge. In this study, we propose a hybrid ordinal regression model that addresses global ordinal structure while enhancing local consistency between neighboring categories. Our approach leverages ordinal metric learning to generate embeddings that capture global ordinal relationships and extends consistent rank logits with a neighbor order penalty in the loss function to reduce adjacent category misclassifications. Experimental results on multiple benchmark ordinal datasets demonstrate that our model significantly minimizes neighboring misclassification errors and global order inconsistencies, outperforming existing ordinal regression models.