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.
CITATION STYLE
Chen, R., Sivakumar, K., & Kargupta, H. (2004). Collective Mining of Bayesian Networks from Distributed Heterogeneous Data. Knowledge and Information Systems, 6(2), 164–187. https://doi.org/10.1007/s10115-003-0107-8
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