Distributed cooperative mining for information consortia
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiagent Collaborative Learning for Distributed Business Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Knowledge and Information Systems
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Bayesian network based business information retrieval model
Knowledge and Information Systems
Dynamic update of data analysis models in emergency systems
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Learning distributed bayesian network structure using majority-based method
Journal of Computational Methods in Sciences and Engineering
Privacy-preserving Bayesian network parameter learning
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Privacy-preserving approach to bayesian network structure learning from distributed data
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Automatic construction of bayesian network structures by means of a concurrent search mechanism
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Building graphical model based system in sensor networks
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
Efficient peer-to-peer belief propagation
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
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We present a collective approach to learning a Bayesian network from distributed heterogeneous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, which models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.