A new restricted Bayesian network classifier

Authors:
Hongbo Shi;Zhihai Wang;Geoffrey I. Webb;Houkuan Huang
Affiliations:
School of Computer and Information Technology, Northern Jiaotong University, Beijing, China and School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia;School of Computer and Information Technology, Northern Jiaotong University, Beijing, China and School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia;School of Computer and Information Technology, Northern Jiaotong University, Beijing, China and School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia;School of Computer and Information Technology, Northern Jiaotong University, Beijing, China and School of Computer Science and Software Engineering, Monash University, Clayton, Victoria, Australia
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

Citing 6
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning belief networks from data: an information theory based approach

CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Lazy Learning of Bayesian Rules

Machine Learning
Comparing Bayesian network classifiers

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

On the basis of examining the existing restricted Bayesian network classifiers, a new Bayes-theorem-based and more strictly restricted Bayesian-network-based classification model DLBAN is proposed, which can be viewed as a double-level Bayesian network augmented naive Bayes classification. The experimental results show that the DLBAN classifier is better than the TAN classifier in the most cases.