ponencia en congreso.page.titleprefix Prediction in health domain using Bayesian networks optimization based on induction learning techniques
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Date
2006
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Abstract
"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."
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TEORIA BAYESIANA DE DECISIONES ESTADISTICAS, APRENDIZAJE