Prediction in health domain using Bayesian networks optimization based on induction learning techniques
Prediction in health domain using Bayesian networks optimization based on induction learning techniques
Archivos
Fecha
2006
Autores
Felgaer, Pablo
Britos, Paola Verónica
García Martínez, Ramón
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
"A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and exible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning.
In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain."