BNT Structure Learning Package : Documentation and Experiments

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BNT Structure Learning Package : Documentation and Experiments

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BNT Structure Learning Package : Documentation and Experiments Philippe Leray, Olivier Francois TECHNICAL REPORT Laboratoire PSI - INSA Rouen- FRE CNRS 2645 BP 08 - Av. de l’Université, 76801 St-Etienne du Rouvray Cedex {Philippe.Leray, Olivier.Francois}@insa-rouen.fr 19th October 2004, Version 1.3

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Abstract Bayesian networks are a formalism for probabilistic reasonning that is more and more used for classification task in data-mining. In some situations, the network structure is given by an ex- pert, otherwise, retrieving it from a database is a NP-hard problem, notably because of the search space complexity. In the last decade, lot of methods have been introduced to learn the network structure automatically, by simplifying the search space (augmented naive bayes, K2) or by using an heuristic in this search space (greedy search). Most of these methods deal with completely observed data, but some others can deal with incomplete data (SEM, MWST-EM). The Bayes Net Toolbox introduced by [Murphy, 2001a] for Matlab allows us using Bayesian Networks or learning them. But this toolbox is not ’state of the art’ if we want to perform a Structural Learning, that’s why we propose this package. Keywords Bayesian Networks, Structure Learning, Classification. 1

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1 Introduction Bayesian networks are probabilistic graphical models introduced by [Kim & Pearl, 1987], [Lauritzen & Speigelhalter, 1988], [Jensen, 1996], [Jordan, 1998].

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