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., CARBONERO RUZ, MARIANO, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, BECERRA ALONSO, DAVID, SÁNCHEZ MONEDERO, JAVIER
Publicación externa No
Medio Int. Conf. Intell. Syst. Des. Appl., ISDA
Alcance Conference Paper
Naturaleza Científica
Ámbito Internacional
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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