SÁNCHEZ MONEDERO, JAVIER, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Gutiérrez P.A. , Hervás-Martínez C.
No
Int. Conf. Intell. Syst. Des. Appl., ISDA
Conference Paper
Científica
01/01/2011
2-s2.0-84857589580
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.
Class labels; Data sets; Machine learning communities; Mathematical basis; Method improvement; Numerical variables; Ordinal classification; Ordinal regression; Ordinal structure; Regression model; Regression problem; Research areas; Support vector; Training algorithms; Intelligent systems; Learning algorithms; Neural networks; Probability distributions; Regression analysis; Support vector machines; Systems analysis; Classification (of information)