@article{262024,
author = "Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and V{\'i}ctor Manuel Vargas",
abstract = "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.",
awards = "JCR (2023): 7.5, Position: 12/180 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.",
comments = "JCR (2023): 7.5, Position: 12/180 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.",
doi = "10.1016/j.engappai.2024.108462",
issn = "1873-6769",
journal = "Engineering Applications of Artificial Intelligence",
keywords = "Ordinal classification, Neural networks, Cumulative link models, Short-term prediction, Significant wave height, Flux of energy, Loss functions",
month = "July",
note = "JCR (2023): 7.5, Position: 12/180 (Q1D1), Category: ENGINEERING, MULTIDISCIPLINARY.",
number = "E",
pages = "108462",
title = "{ORFEO}: {O}rdinal classifier and {R}egressor {F}usion for {E}stimating an {O}rdinal categorical target",
url = "www.sciencedirect.com/science/article/pii/S0952197624006201?via%3Dihub",
volume = "133",
year = "2024",
}