A multivariate discretization method for learning Bayesian networks from mixed data
Monti S, Cooper GF. A multivariate discretization method for learning Bayesian networks from mixed data. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (1998) 404-413.
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.