Improving classification performance by combining multiple TAN classifiers

Authors:
Hongbo Shi;Zhihai Wang;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
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

Citing 6
Cited 0

The Strength of Weak Learnability

Machine Learning
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
An adaptive version of the boost by majority algorithm

COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Naive Bayesian Classifier Committees

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Bagging, boosting, and C4.S

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it does not have much more improvements on a stable learning algorithm. TAN, Tree-Augmented Naive Bayes, is a tree-like Bayesian network. The standard TAN learning algorithm generates a stable TAN classifier, which is difficult to improve its accuracy by boosting technique. In this paper, a new TAN learning algorithm called GTAN and a TAN classifier combination method called Boosting-MultiTAN are presented. Through comparisons of this TAN classifier combination method with the standard TAN classifier in the experiments, the Boosting-MultiTAN shows higher classification accuracy than the standard TAN classifier on the most data sets.