Leveraging ordinal generalized matrix learning vector quantization for improved classification

This paper introduces Ordinal Generalized Matrix Learning Vector Quantization (ORGMLVQ), an enhanced version of the GMLVQ algorithm designed for classifying data with an inherent order among classes. ORGMLVQ incorporates ordinal constraints directly into the metric learning process, allowing the model to better capture the progression between categories-an important aspect in applications such as medical diagnostics or risk grading. Through experiments on multiple ordinal regression datasets, as well as standard UCI benchmarks and real-world problems, the proposed method demonstrates significant improvement of MAUC while maintaining the interpretability and prototype-based nature of the original GMLVQ. These results suggest that our method is a strong, interpretable alternative for learning from structured, ordered data.