MORCU: Margin-based ordinal classification with dynamic regularization for calibration and unimodality

Abstract

Confidence calibration is crucial for accurate and reliable ordinal classification, yet it remains largely overlooked, with existing calibration studies rarely addressing the unique challenges posed by ordered class labels. We introduce Margin-based Ordinal Classification with Dynamic Regularization for Calibration and Unimodality (MORCU). It combines dynamic log-barrier regularization to enforce structured probability distributions with our Target-Preserving Margin Penalty (TPMP), a newly introduced approach that refines adjacent non-target logits to promote calibration and unimodality. By adaptively balancing structural constraints and confidence estimation, MORCU mitigates both overconfidence and underconfidence, producing well-calibrated probability distributions aligned with ordinal relationships. Experimental results across diverse benchmark datasets demonstrate consistent calibration gains and competitive ordinal classification performance, making it well-suited for applications requiring both predictive accuracy and trustworthy confidence estimation. The code is publicly available at https://github.com/labhai/MORCU.