Learning Bayesian Belief Network Classifiers: Algorithms and System
- Jie Cheng
- Russ Greiner, Dept of Computing Science; PI of AICML
This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) -- primarily unrestricted Bayesian networks and Bayesian multi-nets. We present our algorithms for learning these classifiers, and discuss how these methods address the overfitting problem and provide a natural method for feature subset selection. Using a set of standard classification problems, we empirically evaluate the performance of various BN-based classifiers. The results show that the proposed BN and Bayes multi-net classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. These results argue that BN based classifiers deserve more attention in the data mining community.
Citation
J. Cheng, R. Greiner. "Learning Bayesian Belief Network Classifiers: Algorithms and System". Canadian Conference on Artificial Intelligence (CAI), Ottawa, Canada, May 2001.| Keywords: | empirical study, bayesian classifier, belief nets, probabilistic graphical models, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Cheng+Greiner:CAI01,
author = {Jie Cheng and Russ Greiner},
title = {Learning Bayesian Belief Network Classifiers: Algorithms and System},
booktitle = {Canadian Conference on Artificial Intelligence (CAI)},
year = 2001,
}Last Updated: April 24, 2007Submitted by Russ Greiner
