Comparing Bayesian Network Classifiers
- Jie Cheng
- Russ Greiner, Dept of Computing Science; PI of AICML
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 BNlearning 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.
Citation
J. Cheng, R. Greiner. "Comparing Bayesian Network Classifiers". Conference on Uncertainty in Artificial Intelligence (UAI), pp 101-107, August 1999.| Keywords: | |
| Category: | In Conference |
BibTeX
@incollection{Cheng+Greiner:UAI99,
author = {Jie Cheng and Russ Greiner},
title = {Comparing Bayesian Network Classifiers},
Pages = {101-107},
booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
year = 1999,
}Last Updated: April 24, 2007Submitted by AICML Admin Assistant
