Research and application of structure learning algorithm for Bayesian networks from distributed data
Shaozhong Zhang, Hua Feng Ding, Xiu-Kun Wang, Hongbo Liu · 2004
Bayesian networks have become a popular knowledge representation scheme for probabilistic knowledge. One of the main challenges in Bayesian networks structure learning is the development of inductive learning techniques that scale up to large and possibly physically distributed data sets. We consider an approach to learning the structure of BN from distributed data. This is based on the collective learning strategy, where a local structure is obtained at each site and the global structure is obtained by cross learning and combining. Local learning is used to identify the local structure of local nodes and local links of cross nodes from local sample data sets. Cross learning can find the entire cross-links of cross nodes. Combined together, the local BNs and BN learnt from cross learning and removes any extra local links. We use the collective learning algorithm in distributed flood decision supporting system. The result shows that the Bayesian network structure is the same compare the collective learning algorithm from distributed data sets with learning from concentrated data sets. And meanwhile the collective learning algorithm is more efficient than transmitting all samples to a central site.