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

TL;DR

TOC-UCO offers a comprehensive, preprocessed collection of 46 tabular ordinal datasets with benchmarking tools, addressing the lack of standard datasets in the ordinal classification research field.

Contribution

This paper introduces TOC-UCO, a publicly available repository of 46 preprocessed ordinal datasets with benchmarking resources, facilitating robust validation of new ordinal classification methods.

Findings

Provides 46 curated ordinal datasets with preprocessing details.

Includes 30 randomized train-test partitions for reproducibility.

Supports benchmarking of novel ordinal classification approaches.

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 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 C\'ordoba (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.