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 flexible 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.
In the paper REF , Pablo Felgaer and Paola Britos 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.
PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES
ICEIS (2)
Computer Science