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The stability of a restricted bayesian network: An empirical investigation

  • Hong Bo Shi*
  • , Hou Kuan Huang
  • , Zhi Hai Wang
  • *Corresponding author for this work
  • Beijing Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The stability is an important criterion of evaluating classification algorithms. Bayesian network classifier is one of the most popular classification methods, however, its stability is rarely studied. Tree Augmented Naive Bayes(TAN), and a restricted Bayesian network, have demonstrated stronger whole performance than the other Bayesian classification methods. The purpose of this paper is to study the stability of TAN. Bayesian network classification method and TAN model are firstly introduced, and then an empirical investigation comparing the stability of several typical classification approaches (decision tree, naive Bayes) with TAN are detailedly described. Experimental results show that Tree Augmented Naive Bayes network classifier is stable.

Original languageEnglish
Title of host publication2003 International Conference on Machine Learning and Cybernetics, ICMLC 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages345-349
Number of pages5
ISBN (Print)0780378652, 9780780378650
StatePublished - 2003
Externally publishedYes
Event2nd International Conference on Machine Learning and Cybernetics, ICMLC 2003 - Xi'an, China
Duration: 2 Nov 20035 Nov 2003

Publication series

NameInternational Conference on Machine Learning and Cybernetics
Volume1

Conference

Conference2nd International Conference on Machine Learning and Cybernetics, ICMLC 2003
Country/TerritoryChina
CityXi'an
Period2/11/035/11/03

Keywords

  • Bayesian network
  • Stability
  • TAN
  • Variance

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