EBANO: A novel Ensemble BAsed on uNimodal Ordinal classifiers for the prediction of significant wave height

Research areas:

• Methodology - Ordinal classification

Year:

2024

Type of Publication:

Article

Authors:

• Vargas, Víctor Manuel
• Gómez-Orellana, Antonio Manuel
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César
• Guijo-Rubio, David

Journal:

Knowledge-Based Systems

Volume:

300

Pages:

1-14

ISSN:

1872-7409

BibTex:

@article{252024, author = "V{\'i}ctor Manuel Vargas and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio", 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.", awards = "JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.knosys.2024.112223", issn = "1872-7409", journal = "Knowledge-Based Systems", note = "JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "1-14", title = "{EBANO}: {A} novel {E}nsemble {BA}sed on u{N}imodal {O}rdinal classifiers for the prediction of significant wave height", url = "www.sciencedirect.com/science/article/pii/S0950705124008578?via%3Dihub", volume = "300", year = "2024", }

Note:

JCR (2023): 7.2, Position: 27/197 (Q1), Category: 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.

Comments:

JCR (2023): 7.2, Position: 27/197 (Q1), Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE