TOC-UCO: a comprehensive repository of tabular ordinal classification datasets
Rafael Ayllón-Gavilán 
David Guijo-Rubio 
Antonio Manuel Gómez-Orellana 
Francisco Bérchez-Moreno 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez Neurocomputing
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abstract
An Ordinal Classification (OC) problem corresponds to a special type of classification characterised by the presence of a natural order relationship among the classes. This type of problem, which can be found in a number of real-world applications, has motivated the design and development of many ordinal methodologies over the last years. However, it is important to highlight that the development of the OC field suffers from one main disadvantage: the lack of a comprehensive set of datasets on which novel approaches to the literature are benchmarked. In order to address this objective, this manuscript from the University of Córdoba (UCO), which has previous experience in the OC field, provides the literature with a publicly available repository of tabular data for a robust validation of novel OC approaches, namely TOC-UCO (Tabular Ordinal Classification repository of the UCO). Specifically, this repository includes a set of 46 tabular ordinal datasets that have been preprocessed under a common framework and that have a reasonable number of patterns and an appropriate class distribution. We also provide the sources and preprocessing steps of each dataset, along with details on how to benchmark a novel approach using the TOC-UCO repository. For this, indices for 30 different randomised train-test partitions are provided to facilitate the reproducibility of the experiments.
Keywords
BibTex Citation
@article{Ayllon2026TOC,
author = {Ayll{\' o}n-Gavil{\' a}n, Rafael and Guijo-Rubio, David and G{\' o}mez-Orellana, Antonio Manuel and B{\' e}rchez-Moreno, Francisco and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio},
journal = {Neurocomputing},
doi = {10.1016/j.neucom.2026.133528},
year = {2026},
pages = {133528},
title = {TOC-{UCO}: a comprehensive repository of tabular ordinal classification datasets},
url = {https://www.sciencedirect.com/science/article/pii/S0925231226009252},
howpublished = {https://www.sciencedirect.com/science/article/pii/S0925231226009252},
volume = {684},
}
BibTex Unicode Citation
@article{Ayllon2026TOC,
author = {Ayllón-Gavilán, Rafael and Guijo-Rubio, David and Gómez-Orellana, Antonio Manuel and Bérchez-Moreno, Francisco and Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio},
journal = {Neurocomputing},
doi = {10.1016/j.neucom.2026.133528},
year = {2026},
pages = {133528},
title = {TOC-{UCO}: a comprehensive repository of tabular ordinal classification datasets},
url = {https://www.sciencedirect.com/science/article/pii/S0925231226009252},
howpublished = {https://www.sciencedirect.com/science/article/pii/S0925231226009252},
volume = {684},
}
APA Citation
Ayllón-Gavilán, R., Guijo-Rubio, D., Gómez-Orellana, A. M., Bérchez-Moreno, F., Vargas-Yun, V. M., & Gutiérrez, P. A. (2026). TOC-UCO: a comprehensive repository of tabular ordinal classification datasets. Neurocomputing, 684, 133528. https://doi.org/10.1016/j.neucom.2026.133528
RIS Citation
TY - JOUR
AU - Ayllón-Gavilán, Rafael
AU - Guijo-Rubio, David
AU - Gómez-Orellana, Antonio Manuel
AU - Bérchez-Moreno, Francisco
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
DA - 2026///
PY - 2026
DO - 10.1016/j.neucom.2026.133528
ID - temp_id_882229843618
SP - 133528
T2 - Neurocomputing
TI - TOC-UCO: a comprehensive repository of tabular ordinal classification
datasets
UR - https://www.sciencedirect.com/science/article/pii/S0925231226009252
VL - 684
ER -
CV Citation
R. Ayllón-Gavilán, D. Guijo-Rubio, A.M. Gómez-Orellana (CA), F. Bérchez-Moreno, V.M. Vargas-Yun, P.A. Gutiérrez (5/6). "TOC-UCO: a comprehensive repository of tabular ordinal classification datasets". Neurocomputing, Vol. 684, pp. 133528, 2026. (Q1, IF: 6.5).