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A new hybrid method for Bayesian network learning With dependency constraints

Schulte, Oliver, Frigo, Gustavo, Greiner, Russell, Luo, Wei and Khosravi, Hassan 2009, A new hybrid method for Bayesian network learning With dependency constraints, in CIDM 2009 : Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining, Institute of Electrical and Electronics, Piscataway, N.J., pp. 53-60, doi: 10.1109/CIDM.2009.4938629.

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Title A new hybrid method for Bayesian network learning With dependency constraints
Author(s) Schulte, Oliver
Frigo, Gustavo
Greiner, Russell
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Khosravi, Hassan
Conference name IEEE Symposium on Computational Intelligence and Data Mining (2009 : Nashville, Tennesee)
Conference location Nashville, Tennesee
Conference dates 30 Mar.-2 Apr. 2009
Title of proceedings CIDM 2009 : Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining
Editor(s) [Unknown]
Publication date 2009
Conference series IEEE Symposium on Computational Intelligence and Data Mining
Start page 53
End page 60
Total pages 8
Publisher Institute of Electrical and Electronics
Place of publication Piscataway, N.J.
Summary A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Bayes net. The quantitative aspects are the net parameters. This paper develops a hybrid criterion for learning Bayes net structures that is based on both aspects. We combine model selection criteria measuring data fit with correlation information from statistical tests: Given a sample d, search for a structure G that maximizes score(G, d), over the set of structures G that satisfy the dependencies detected in d. We rely on the statistical test only to accept conditional dependencies, not conditional independencies. We show how to adapt local search algorithms to accommodate the observed dependencies. Simulation studies with GES search and the BDeu/BIC scores provide evidence that the additional dependency information leads to Bayes nets that better fit the target model in distribution and structure.
ISBN 9781424427659
1424427657
Language eng
DOI 10.1109/CIDM.2009.4938629
Field of Research 170203 Knowledge Representation and Machine Learning
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052499

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.