Numerical variable reconstruction from ordinal categories based on probability distributions

Áreas de investigación:

• Methodology - Machine learning

Año:

2011

Tipo de publicación:

Artículo en conferencia

Palabras clave:

ordinal classification, ordinal regression, support vector machine, neural networks

Autores:

• Sánchez-Monedero, Javier
• Carbonero-Ruz, Mariano
• Becerra-Alonso, David
• Martínez-Estudillo, Francisco José
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Título del libro:

11th International Conference on Intelligent Systems Design andApplications (ISDA 2011)

Páginas:

1182-1187

Dirección:

Cordoba, Spain, Spain

Mes:

Noviembre

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 belong 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 numeric 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.