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. • Introduction of a novel ordinal classification repository: TOC-UCO . • Extension of the number of datasets with a high number of classes. • Analysis of the previous ordinal classification benchmarking repository. • In-depth comparison between TOC-UCO and the previous repository. • Presentation of a baseline experimentation on the new TOC-UCO archive.

Ordinal classification, where labels follow a natural order, has gained increasing attention, particularly in the deep learning community due to its relevance in tasks such as age estimation, medical grading, and quality assessment. Despite the growing number of deep ordinal classification methods, a comprehensive experimental analysis of their core ordinal components remains lacking. This work presents a systematic evaluation of deep ordinal classifiers by analysing the impact of three key modelling choices: the loss function, output layer, and labelling strategy. To analyse their effects, we adopt a unified architecture and evaluate one nominal and 19 ordinal configurations, resulting from combination of two loss functions, two output layers, and five labelling strategies. These configurations are assessed on 12 diverse ordinal image datasets using six performance metrics, including both ordinal and nominal measures. Results show that ordinal output layers consistently outperform softmax, and that soft labelling generally improves generalisation. While categorical cross-entropy achieves better average performance, especially on nominal metrics, no configuration performs best across all datasets. Statistical analyses indicate significant interactions between losses, outputs, labelling strategies, and datasets, highlighting the need to adapt methodological choices to specific tasks. These findings provide valuable guidance for designing robust deep ordinal classification models.

In this paper we present a novel methodology, referenced as ORFEO (Ordinal classifier and Regressor Fusion for Estimating an Ordinal categorical target), to enhance the performance in ordinal classification problems for which the latent variable is observable. ORFEO is an artificial neural network model incorporating two outputs, one for ordinal classification, using the cumulative link model, and one for regression, using a linear model. Both outputs are simultaneously optimised considering a loss function that linearly combines both classification and regression losses. The main motivation behind developing the proposed approach is to enhance the performance of a standard ordinal classifier. This improvement is facilitated by considering the regression output, which allows the model to differentiate between patterns within the same category. The ORFEO model is applied to two problems in the field of marine and ocean engineering: short-term prediction of both significant wave height and flux of energy. Both problems are addressed considering four different coastal zones of the United States of America, using 13 datasets formed by buoys measurements and reanalysis data. A comprehensive comparison against 20 methodologies, including regression and nominal/ordinal classification approaches is performed, by using diverse nominal and ordinal performance metrics. Ranks achieved indicate that ORFEO outperforms all the compared methodologies in terms of all the performance measures, demonstrating the efficacy and robustness of the proposal. Finally, a statistical analysis is conducted, concluding that there are statistically significant differences across ordinal and nominal performance metrics in favour of the proposed ORFEO model.

In this study, we present EBANO (Ensemble BAsed on uNimodal Ordinal classifiers), which is a novel ensemble approach of ordinal classifiers that includes four soft labelling approaches along with an ordinal logistic regression model. These models are integrated within the ensemble using a new aggregation methodology that automatically weights each individual classifier using a randomised search algorithm. In addition, the proposed EBANO methodology is applied to tackle short-term prediction of Significant Wave Height (SWH). Thus, we employ EBANO using a diverse set of eight datasets derived from reanalysis data and buoy-recorded SWH measurements. To approach the problem from an ordinal classification perspective, the SWH values are discretised into five ordered classes by applying hierarchical clustering. EBANO is compared with each of the individual classifiers integrated in the proposed ensemble along with a different ensemble technique termed HESCA. Both the average results and the ranks obtained show the superiority of EBANO over the compared methodologies, being more pronounced in the metrics that account for the imbalance present in the datasets considered. Finally, a statistical analysis is performed, confirming the statistical significance of the observed differences in all comparisons. This analysis underscores the effectiveness of EBANO in addressing the problem of SWH prediction, showcasing its excellence.