A multivariate discretization method for learning Bayesian networks from mixed data

Authors:
Stefano Monti;Gregory F. Cooper
Affiliations:
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA;Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA and Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
Venue:
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Year:
1998

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Abstract

In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data containing both continuous and discrete variables. We describe a new technique for multivariate discretization, whereby each continuous variable is discretized while taking into account its interaction with the other variables. The technique is based on the use of a Bayesian scoring metric that scores the discretization policy for a continuous variable given a BN structure and the observed data. Since the metric is relative to the BN structure currently being evaluated, the discretization of a variable needs to be dynamically adjusted as the BN structure changes.