Uncertainty in Artificial Intelligence
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Comparing Bayesian Network Classifiers
Jie Cheng, Russell Greiner
Abstract:
In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BN-learning algorithm. Experimental results show the obtained classifiers, learned using the CI based algorithms, are competitive with (or superior to) the best known classifiers, based on both Bayesian networks and other formalisms; and that the computational time for learning and using these classifiers is relatively small. Moreover, these results also suggest a way to learn yet more effective classifiers; we demonstrate empirically that this new algorithm does work as expected. Collectively, these results argue that BN classifiers deserve more attention in machine learning and data mining communities.
Keywords: Data mining, Classification, Bayesian belief network learning, Bayesian belief networ
Pages: 101-108
PS Link: http://www.cs.ualberta.ca/~jcheng/Doc/bn_clsfr.ps
PDF Link: /papers/99/p101-cheng.pdf
BibTex:
@INPROCEEDINGS{Cheng99,
AUTHOR = "Jie Cheng and Russell Greiner",
TITLE = "Comparing Bayesian Network Classifiers",
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 = "101--108"
}


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