TOC-UCO: a comprehensive repository of tabular ordinal classification datasets

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, that 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 approach this objective, this manuscript from the University of Córdoba (UCO), which has previous experience on 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 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 different randomised train-test partitions are provided to facilitate the reproducibility of the experiments.

@article{ayllon-gavilan2026toc-uco:,
    author = {Rafael Ayllón-Gavilán and David Guijo-Rubio and Antonio Manuel Gómez-Orellana and Francisco Bérchez-Moreno and Víctor Manuel Vargas and Pedro Antonio Gutiérrez},
    title = {TOC-UCO: a comprehensive repository of tabular ordinal classification datasets},
    journal = {Neurocomputing},
    year = {2026},
    volume = {null},
    number = {null},
    pages = {133528--133528},
    doi = {10.1016/j.neucom.2026.133528}
}

Ayllón-Gavilán, R., Guijo-Rubio, D., Gómez-Orellana, A., Bérchez-Moreno, F., Vargas, V., Gutiérrez, P. (2026). TOC-UCO: a comprehensive repository of tabular ordinal classification datasets. Neurocomputing, null(null), 133528-133528.

R. Ayllón-Gavilán, D. Guijo-Rubio, A.M. Gómez-Orellana (CA), F. Bérchez-Moreno, V.M. Vargas, P.A. Gutiérrez, (5/6) "TOC-UCO: a comprehensive repository of tabular ordinal classification datasets", Neurocomputing, Vol. null(null), pp. 133528-133528, 2026. (Q1, IF: 5.5)

TY  - JOUR
T1  - TOC-UCO: a comprehensive repository of tabular ordinal classification datasets
AU  - Ayllón-Gavilán, Rafael
AU  - Guijo-Rubio, David
AU  - Gómez-Orellana, Antonio Manuel
AU  - Bérchez-Moreno, Francisco
AU  - Vargas, Víctor Manuel
AU  - Gutiérrez, Pedro Antonio
JO  - Neurocomputing
VL  - null
IS  - null
SP  - 133528
EP  - 133528
PY  - 2026
DO  - 10.1016/j.neucom.2026.133528
ER  -