Uncertainty in Artificial Intelligence
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A Multivariate Discretization Method for Learning Bayesian Networks from Mixed Data
Stefano Monti, Gregory Cooper
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.
Keywords:
Pages: 404-413
PS Link: http://www.isp.pitt.edu/~smonti/HTML/DOCUMENTS/uai98.html
PDF Link: /papers/98/p404-monti.pdf
BibTex:
@INPROCEEDINGS{Monti98,
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"
}


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