J. Cheng and R. Greiner, “Comparing Bayesian Network Classifiers,” In: K. B. Laskey and H. Prade, Ed., Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Fran- cisco, 15 October 1999, pp. 101-108.

has been cited by the following article:

• TITLE: Experiments with Two New Boosting Algorithms

  AUTHORS: Xiaowei Sun, Hongbo Zhou

  KEYWORDS: Boosting, Combination Method, TAN, BAN, Bayesian Network Classifier

  JOURNAL NAME: Intelligent Information Management, Vol.2 No.6, June 29, 2010

  ABSTRACT: Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it dose not make much more improvement of a stable learning algorithm. In this paper, multiple TAN classifiers are combined by a combination method called Boosting-MultiTAN that is compared with the Boosting-BAN classifier which is boosting based on BAN combination. We describe experiments that carried out to assess how well the two algorithms perform on real learning problems. Fi- nally, experimental results show that the Boosting-BAN has higher classification accuracy on most data sets, but Boosting-MultiTAN has good effect on others. These results argue that boosting algorithm deserve more attention in machine learning and data mining communities.