Learning Local Components to Understand Large Bayesian Networks

Yifeng Zeng, Yanping Xiang, Jorge Cordero, Yujian Lin

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1 Citationer (Scopus)

Abstrakt

Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data.
OriginalsprogEngelsk
TitelIEEE International Conference on Data Mining (ICDM 2009)
ForlagIEEE Computer Society Press
Publikationsdato2009
Udgave9
Sider1076-1081
ISBN (Trykt)978-0-7695-3895-2
DOI
StatusUdgivet - 2009
BegivenhedIEEE International Conference on Data Mining (ICDM 2009) - Miami, Florida, USA
Varighed: 6 dec. 20099 jan. 2010
Konferencens nummer: 9

Konference

KonferenceIEEE International Conference on Data Mining (ICDM 2009)
Nummer9
Land/OmrådeUSA
ByMiami, Florida
Periode06/12/200909/01/2010
NavnProceedings of the 2009 Ninth IEEE International Conference on Data Mining
ISSN1550-4786

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