luo-newhybrid-2009.pdf (874.76 kB)
A new hybrid method for Bayesian network learning With dependency constraints
conference contribution
posted on 2009-01-01, 00:00 authored by O Schulte, G Frigo, R Greiner, Wei LuoWei Luo, H KhosraviA 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.
History
Event
IEEE Symposium on Computational Intelligence and Data Mining (2009 : Nashville, Tennesee)Pagination
53 - 60Publisher
Institute of Electrical and ElectronicsLocation
Nashville, TenneseePlace of publication
Piscataway, N.J.Publisher DOI
Start date
2009-03-30End date
2009-04-02ISBN-13
9781424427659ISBN-10
1424427657Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2009, IEEETitle of proceedings
CIDM 2009 : Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data MiningUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC