Distributed Bayesian network learning algorithm based on model fusion
Kui-Xiang Gou · Jisuanji yingyong yanjiu · 2010
For learning Bayesian network structure from homogeneous datasets,this paper proposed a distributed algorithm.It firstly learned each local structure using score method,then with the expectations of mutual information and conditional mutual information as evaluated criterion,it fused these local structures to obtain global structure.For using only mutual information and conditional mutual information of variation,without obtaining directly sample data,it might effectively protect privacy.Simulating the algorithm on Alarm dataset,the ratio of error is less than 6%,the running time of the algorithm is shorter than the running time of collective algorithms,the algotithm is valid.