Leveraging ordinal generalized matrix learning vector quantization for improved classification
File(s)
Published version
Author(s)
Abdi, Lida
Prete, Alessandro
Arlt, Wiebke
Biehl, Michael
Type
Journal Article
Abstract
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.
Date Issued
2026-03-16
Date Acceptance
2026-02-03
Citation
Neural Computing and Applications, 2026, 38 (6)
ISSN
0941-0643
Publisher
Springer Science and Business Media LLC
Journal / Book Title
Neural Computing and Applications
Volume
38
Issue
6
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
174
Date Publish Online
2026-03-16