Aprendizagem Estrutural de Redes Bayesianas Utilizando Métrica MDL Modificada (Structural Learning of Bayesian Networks using a modified MDL score metric)

Aderson Cleber Pifer (acpifer@dca.ufrn.br), Luiz Affonso Guedes (affonso@dca.ufrn.br)

Universidade Federal do Rio Grande do Norte
This paper appears in: Revista IEEE América Latina

Publication Date: Dec. 2007
Volume: 5,   Issue: 8 
ISSN: 1548-0992

Bayesian networks are tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This paper address learn the structure of ALARM pattern benchmark using K-2 algorithm and a modified MDL as score metric. Results shown that score metrics with parameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures and that modified MDL gives better results than original MDL.

Index Terms:
Bayesian Networks, K-2, MDL, ALARM, Score Metric, Structural Learning   

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