Comparing Bayesian network classifiers

Authors:
Jie Cheng;Russell Greiner
Affiliations:
Department of Computing Science, University of Alberta, Alberta, Canada;Department of Computing Science, University of Alberta, Alberta, Canada
Venue:
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Year:
1999

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Abstract

In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naïve-Bayes, tree augmented Naïve-Bayes, BN augmented Naïve-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.