An exhaustive ordinal ECOC framework for ordinal classification
Francisco Bérchez-Moreno 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez International work-conference on the interplay between natural and artificial computation
Abstract
Error-Correcting Output Codes (ECOC) decompose multi-class problems into a set of simpler subproblems, providing robustness through redundancy. In ordinal classification, however, standard ECOC designs may violate the natural order structure of the labels. In this work, we introduce an exhaustive ordinal ECOC framework that systematically generates all monotone partitions of the ordered class set. The proposed construction is obtained by considering every non-empty subset of admissible threshold positions, thereby deriving the complete family of contiguous ordinal groupings. The resulting coding matrix preserves ordinal consistency while increasing structural redundancy in a principled manner. Experimental evaluation on 46 benchmark ordinal datasets, using logistic regression as base learner and 30 independent runs, shows that the proposed method consistently outperforms both a nominal ECOC baseline and strong ordinal competitors. Statistical analysis confirms that the improvements are significant across datasets. These results demonstrate that exploiting the full space of monotone ordinal decompositions yields richer structural information than classical ordered partitions.
BibTex Citation
@inproceedings{Berchez2026exhaustive,
author = {B{\' e}rchez-Moreno, Francisco and Vargas-Yun, V{\' i}ctor Manuel and Su{\' a}rez, Alberto and {\' A}lvarez-P{\' e}rez, Lorena and Guti{\' e}rrez, Pedro Antonio},
booktitle = {International work-conference on the interplay between natural and artificial computation},
doi = {10.1007/978-3-032-27317-8_46},
year = {2026},
pages = {486--495},
title = {An exhaustive ordinal {ECOC} framework for ordinal classification},
url = {https://link.springer.com/chapter/10.1007/978-3-032-27317-8_46},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-032-27317-8\textunderscore{}46},
}
BibTex Unicode Citation
@inproceedings{Berchez2026exhaustive,
author = {Bérchez-Moreno, Francisco and Vargas-Yun, Víctor Manuel and Suárez, Alberto and {\' A}lvarez-Pérez, Lorena and Gutiérrez, Pedro Antonio},
booktitle = {International work-conference on the interplay between natural and artificial computation},
doi = {10.1007/978-3-032-27317-8_46},
year = {2026},
pages = {486--495},
title = {An exhaustive ordinal {ECOC} framework for ordinal classification},
url = {https://link.springer.com/chapter/10.1007/978-3-032-27317-8_46},
howpublished = {https://link.springer.com/chapter/10.1007/978-3-032-27317-8\textunderscore{}46},
}
APA Citation
Bérchez-Moreno, F., Vargas-Yun, V. M., Suárez, A., Álvarez-Pérez, L., & Gutiérrez, P. A. (2026). An exhaustive ordinal ECOC framework for ordinal classification. International Work-Conference on the Interplay between Natural and Artificial Computation, 486–495. https://doi.org/10.1007/978-3-032-27317-8_46
RIS Citation
TY - CONF
AU - Bérchez-Moreno, Francisco
AU - Vargas-Yun, Víctor Manuel
AU - Suárez, Alberto
AU - Álvarez-Pérez, Lorena
AU - Gutiérrez, Pedro Antonio
C3 - International work-conference on the interplay between natural and art
ificial computation
DA - 2026///
C2 - 2026
DO - 10.1007/978-3-032-27317-8_46
ID - temp_id_895789109523
SP - 486-495
TI - An exhaustive ordinal ECOC framework for ordinal classification
UR - https://link.springer.com/chapter/10.1007/978-3-032-27317-8_46
ER -
CV Citation
F. Bérchez-Moreno (CA), V.M. Vargas-Yun, A. Suárez, L. Álvarez-Pérez, P.A. Gutiérrez (2/5). "An exhaustive ordinal ECOC framework for ordinal classification". International work-conference on the interplay between natural and artificial computation, pp. 486–495, 2026.