| Título |
Numerical variable reconstruction from ordinal categories based on probability distributions |
| Autores |
SÁNCHEZ MONEDERO, JAVIER, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Gutiérrez P.A. , Hervás-Martínez C. |
| Publicación externa |
No |
| Medio |
Int. Conf. Intell. Syst. Des. Appl., ISDA |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857589580&doi=10.1109%2fISDA.2011.6121819&partnerID=40&md5=05b934c1dc76426f06b2f6db11f644ed |
| Fecha de publicacion |
01/01/2011 |
| Scopus Id |
2-s2.0-84857589580 |
| DOI |
10.1109/ISDA.2011.6121819 |
| Abstract |
Ordinal classification problems are an active research area in the machine learning community. Many previous works adapted state-of-art nominal classifiers to improve ordinal classification so that the method can take advantage of the ordinal structure of the dataset. However, these method improvements often rely upon a complex mathematical basis and they usually are attached to the training algorithm and model. This paper presents a novel method for generally adapting classification and regression models, such as artificial neural networks or support vector machines. The ordinal classification problem is reformulated as a regression problem by the reconstruction of a numerical variable which represents the different ordered class labels. Despite the simplicity and generality of the method, results are competitive in comparison with very specific methods for ordinal regression. © 2011 IEEE. |
| Palabras clave |
Class labels; Data sets; Machine learning communities; Mathematical basis; Method improvement; Numerical variables; Ordinal classification; Ordinal regression; Ordinal structure; Regression model; Reg |
| Miembros de la Universidad Loyola |
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