TOC-UCO: a comprehensive repository of tabular ordinal classification datasets
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918103072374784 |
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| author | Ayllón-Gavilán, Rafael Guijo-Rubio, David Gómez-Orellana, Antonio Manuel Bérchez-Moreno, Francisco Vargas-Yun, Víctor Manuel Gutiérrez, Pedro A. |
| author_facet | Ayllón-Gavilán, Rafael Guijo-Rubio, David Gómez-Orellana, Antonio Manuel Bérchez-Moreno, Francisco Vargas-Yun, Víctor Manuel Gutiérrez, Pedro A. |
| contents | 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 can be found in a number of real-world applications, motivating 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 have to be benchmarked. In order to approach this objective, this manuscript from the University of Córdoba (UCO), which have 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 $46$ tabular ordinal datasets, preprocessed under a common framework and ensured to 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TOC-UCO: a comprehensive repository of tabular ordinal classification datasets Ayllón-Gavilán, Rafael Guijo-Rubio, David Gómez-Orellana, Antonio Manuel Bérchez-Moreno, Francisco Vargas-Yun, Víctor Manuel Gutiérrez, Pedro A. Machine Learning 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 can be found in a number of real-world applications, motivating 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 have to be benchmarked. In order to approach this objective, this manuscript from the University of Córdoba (UCO), which have 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 $46$ tabular ordinal datasets, preprocessed under a common framework and ensured to 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. |
| title | TOC-UCO: a comprehensive repository of tabular ordinal classification datasets |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.17348 |