Uncertainty in Artificial Intelligence
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Estimating Continuous Distributions in Bayesian Classifiers
George John, Pat Langley
Abstract:
When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.
Keywords: Naive Bayesian classifier, kernel density estimation.
Pages: 338-345
PS Link: ftp://robotics.stanford.edu/pub/gjohn/papers/flex.ps
PDF Link: /papers/95/p338-john.pdf
BibTex:
@INPROCEEDINGS{John95,
AUTHOR = "George John and Pat Langley",
TITLE = "Estimating Continuous Distributions in Bayesian Classifiers",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "338--345"
}


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