EBANO: A novel Ensemble BAsed on uNimodal Ordinal classifiers for the prediction of significant wave height
Víctor Manuel Vargas-Yun 
Antonio Manuel Gómez-Orellana 
Pedro Antonio Gutiérrez 
César Hervás-Martínez 
David Guijo-Rubio Knowledge-Based Systems
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abstract
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.
BibTex Citation
@article{Vargas2024EBANO,
author = {Vargas-Yun, V{\' i}ctor Manuel and G{\' o}mez-Orellana, Antonio Manuel and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar and Guijo-Rubio, David},
journal = {Knowledge-Based Systems},
doi = {10.1016/j.knosys.2024.112223},
year = {2024},
pages = {112223},
title = {EBANO: A novel {Ensemble} {BAsed} on {uNimodal} {Ordinal} classifiers for the prediction of significant wave height},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124008578?via%3Dihub},
howpublished = {https://www.sciencedirect.com/science/article/pii/S0950705124008578?via\%3Dihub},
volume = {300},
}
BibTex Unicode Citation
@article{Vargas2024EBANO,
author = {Vargas-Yun, Víctor Manuel and Gómez-Orellana, Antonio Manuel and Gutiérrez, Pedro Antonio and Hervás-Martínez, César and Guijo-Rubio, David},
journal = {Knowledge-Based Systems},
doi = {10.1016/j.knosys.2024.112223},
year = {2024},
pages = {112223},
title = {EBANO: A novel {Ensemble} {BAsed} on {uNimodal} {Ordinal} classifiers for the prediction of significant wave height},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124008578?via%3Dihub},
howpublished = {https://www.sciencedirect.com/science/article/pii/S0950705124008578?via\%3Dihub},
volume = {300},
}
APA Citation
Vargas-Yun, V. M., Gómez-Orellana, A. M., Gutiérrez, P. A., Hervás-Martínez, C., & Guijo-Rubio, D. (2024). EBANO: A novel Ensemble BAsed on uNimodal Ordinal classifiers for the prediction of significant wave height. Knowledge-Based Systems, 300, 112223. https://doi.org/10.1016/j.knosys.2024.112223
RIS Citation
TY - JOUR
AU - Vargas-Yun, Víctor Manuel
AU - Gómez-Orellana, Antonio Manuel
AU - Gutiérrez, Pedro Antonio
AU - Hervás-Martínez, César
AU - Guijo-Rubio, David
DA - 2024///
PY - 2024
DO - 10.1016/j.knosys.2024.112223
ID - temp_id_101331111482
SP - 112223
T2 - Knowledge-Based Systems
TI - EBANO: A novel Ensemble BAsed on uNimodal Ordinal classifiers for the
prediction of significant wave height
UR - https://www.sciencedirect.com/science/article/pii/S0950705124008578?vi
a%3Dihub
VL - 300
ER -
CV Citation
V.M. Vargas-Yun, A.M. Gómez-Orellana (CA), P.A. Gutiérrez, C. Hervás-Martínez, D. Guijo-Rubio (1/5). "EBANO: A novel Ensemble BAsed on uNimodal Ordinal classifiers for the prediction of significant wave height". Knowledge-Based Systems, Vol. 300, pp. 112223, 2024. (Q1, IF: 7.6).