Uncertainty in Artificial Intelligence
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A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model
Stefano Monti, Gregory Cooper
Abstract:
In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification performance, both in terms of accuracy and in terms of calibration of the estimated probabilities. The proposed classifier is obtained by superimposing a finite mixture model on the set of feature variables of a naive Bayes model. We present experimental results that compare the predictive performance on real datasets of the new classifier with the predictive performance of the NB classifier and the FM classifier.
Keywords: Classification, Clustering, Calibration
Pages: 447-456
PS Link: http://www.isp.pitt.edu/~smonti/DOCUMENTS/uai99.ps
PDF Link: /papers/99/p447-monti.pdf
BibTex:
@INPROCEEDINGS{Monti99,
AUTHOR = "Stefano Monti and Gregory Cooper",
TITLE = "A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "447--456"
}


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