A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
Stefano Monti, Gregory Cooper
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.
PS Link: http://www.isp.pitt.edu/~smonti/HTML/DOCUMENTS/uai98.html
PDF Link: /papers/98/p404-monti.pdf
AUTHOR = "Stefano Monti
and Gregory Cooper",
TITLE = "A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "404--413"