Energy Flux Prediction Using an Ordinal Soft Labelling Strategy
Antonio Manuel Gómez-Orellana 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez 
Jorge Pérez-Aracil 
Sancho Salcedo-Sanz 
César Hervás-Martínez 
David Guijo-Rubio Bioinspired systems for translational applications: From robotics to social engineering
Abstract
This paper addresses the problem of short-term energy flux prediction. For this purpose, we propose the use of an ordinal classification neural network model optimised using the triangular regularised categorical cross-entropy loss, termed MLP-T. This model is based on a soft labelling strategy, that replaces the crisp 0/1 labels on the loss computation with soft versions encoding the ordinal information. This soft label encoding leverages the inherent ordering between categories to reduce the cost of ordinal classification errors and improve model generalisation performance. Specifically, the soft labels for each target class are derived from triangular probability distributions. To assess the performance of MLP-T, six datasets built from buoy measurements and reanalysis data have been used. MLP-T has been compared to nominal and ordinal classification techniques in terms of four performance metrics. MLP-T achieved an outstanding performance across all datasets and performance metrics, securing the best mean results. Despite the imbalanced nature of the problem, which makes the ordinal classification task notably difficult, MLP-T achieved good results in all classes across all datasets, including the underrepresented classes. Remarkably, MLP-T was the only approach that correctly classified at least one instance of the minority class in all datasets. Furthermore, MLP-T secured the top rank in all cases, confirming its suitability for the problem addressed.
Keywords
BibTex Citation
@inproceedings{Gomez2024Energy,
author = {G{\' o}mez-Orellana, Antonio Manuel and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio and P{\' e}rez-Aracil, Jorge and Salcedo-Sanz, Sancho and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar and Guijo-Rubio, David},
booktitle = {Bioinspired systems for translational applications: From robotics to social engineering},
doi = {10.1007/978-3-031-61137-7_26},
year = {2024},
pages = {283--292},
title = {Energy {Flux} {Prediction} {Using} an {Ordinal} {Soft} {Labelling} {Strategy}},
url = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7_26},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7\textunderscore{}26},
volume = {14675},
}
BibTex Unicode Citation
@inproceedings{Gomez2024Energy,
author = {Gómez-Orellana, Antonio Manuel and Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio and Pérez-Aracil, Jorge and Salcedo-Sanz, Sancho and Hervás-Martínez, César and Guijo-Rubio, David},
booktitle = {Bioinspired systems for translational applications: From robotics to social engineering},
doi = {10.1007/978-3-031-61137-7_26},
year = {2024},
pages = {283--292},
title = {Energy {Flux} {Prediction} {Using} an {Ordinal} {Soft} {Labelling} {Strategy}},
url = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7_26},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-031-61137-7\textunderscore{}26},
volume = {14675},
}
APA Citation
Gómez-Orellana, A. M., Vargas-Yun, V. M., Gutiérrez, P. A., Pérez-Aracil, J., Salcedo-Sanz, S., Hervás-Martínez, C., & Guijo-Rubio, D. (2024). Energy Flux Prediction Using an Ordinal Soft Labelling Strategy. Bioinspired Systems for Translational Applications: From Robotics to Social Engineering, 14675, 283–292. https://doi.org/10.1007/978-3-031-61137-7_26
RIS Citation
TY - CONF
AU - Gómez-Orellana, Antonio Manuel
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
AU - Pérez-Aracil, Jorge
AU - Salcedo-Sanz, Sancho
AU - Hervás-Martínez, César
AU - Guijo-Rubio, David
C3 - Bioinspired systems for translational applications: From robotics to s
ocial engineering
DA - 2024///
C2 - 2024
DO - 10.1007/978-3-031-61137-7_26
ID - temp_id_476837342427
SP - 283-292
TI - Energy Flux Prediction Using an Ordinal Soft Labelling Strategy
UR - https://link.springer.com/chapter/10.1007/978-3-031-61137-7_26
VL - 14675
ER -
CV Citation
A.M. Gómez-Orellana, V.M. Vargas-Yun (CA), P.A. Gutiérrez, J. Pérez-Aracil, S. Salcedo-Sanz, C. Hervás-Martínez, D. Guijo-Rubio (2/7). "Energy Flux Prediction Using an Ordinal Soft Labelling Strategy". Bioinspired systems for translational applications: From robotics to social engineering, pp. 283–292, 2024.