dlordinal: A Python package for deep ordinal classification

Authors

Francisco Bérchez-Moreno

Rafael Ayllón-Gavilán

Víctor Manuel Vargas-Yun

David Guijo-Rubio

César Hervás-Martínez

Juan Carlos Fernández

Pedro Antonio Gutiérrez

Published in Journal

Neurocomputing

COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Impact Factor JCR 2024

6.5

JCR Ranking

37 / 204

Position

ISSN 1872-8286

Vol. 622

Pages 129305

Abstract

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal

Keywords

deep learning ordinal classification ordinal regression python pytorch soft labelling

BibTex Citation

@article{Berchez2025dlordinal,
	author = {B{\' e}rchez-Moreno, Francisco and Ayll{\' o}n-Gavil{\' a}n, Rafael and Vargas-Yun, V{\' i}ctor Manuel and Guijo-Rubio, David and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar and Fern{\' a}ndez, Juan Carlos and Guti{\' e}rrez, Pedro Antonio},
	journal = {Neurocomputing},
	doi = {10.1016/j.neucom.2024.129305},
	year = {2025},
	pages = {129305},
	title = {dlordinal: A {Python} package for deep ordinal classification},
	url = {https://doi.org/10.1016/j.neucom.2024.129305},
	howpublished = {https://doi.org/10.1016/j.neucom.2024.129305},
	volume = {622},
}

BibTex Unicode Citation

@article{Berchez2025dlordinal,
	author = {Bérchez-Moreno, Francisco and Ayllón-Gavilán, Rafael and Vargas-Yun, Víctor Manuel and Guijo-Rubio, David and Hervás-Martínez, César and Fernández, Juan Carlos and Gutiérrez, Pedro Antonio},
	journal = {Neurocomputing},
	doi = {10.1016/j.neucom.2024.129305},
	year = {2025},
	pages = {129305},
	title = {dlordinal: A {Python} package for deep ordinal classification},
	url = {https://doi.org/10.1016/j.neucom.2024.129305},
	howpublished = {https://doi.org/10.1016/j.neucom.2024.129305},
	volume = {622},
}

APA Citation

Bérchez-Moreno, F., Ayllón-Gavilán, R., Vargas-Yun, V. M., Guijo-Rubio, D., Hervás-Martínez, C., Fernández, J. C., & Gutiérrez, P. A. (2025). dlordinal: A Python package for deep ordinal classification. Neurocomputing, 622, 129305. https://doi.org/10.1016/j.neucom.2024.129305

RIS Citation

TY  - JOUR
AU  - Bérchez-Moreno, Francisco
AU  - Ayllón-Gavilán, Rafael
AU  - Vargas-Yun, Víctor Manuel
AU  - Guijo-Rubio, David
AU  - Hervás-Martínez, César
AU  - Fernández, Juan Carlos
AU  - Gutiérrez, Pedro Antonio
DA  - 2025///
PY  - 2025
DO  - 10.1016/j.neucom.2024.129305
ID  - temp_id_464851853155
SP  - 129305
T2  - Neurocomputing
TI  - dlordinal: A Python package for deep ordinal classification
UR  - https://doi.org/10.1016/j.neucom.2024.129305
VL  - 622
ER  -

CV Citation

F. Bérchez-Moreno, R. Ayllón-Gavilán, V.M. Vargas-Yun (CA), D. Guijo-Rubio, C. Hervás-Martínez, J.C. Fernández, P.A. Gutiérrez (3/7). "dlordinal: A Python package for deep ordinal classification". Neurocomputing,  Vol. 622, pp. 129305, 2025. (Q1, IF: 6.5).