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MMOS+ ordering search Method for Bayesian Network Structure Learning and Its Application

Chinese Journal of Electronics 2020年第1期29卷 147-153页

作者： HE Chuchao GAO Xiaoguang WAN Kaifang School of Electronic Information Northwestern Polytechnic University

To address the problem of a reduced efficiency due to an increase of the search space, it has been proposed that priors could be added as constraints to the OS+ algorithm, which are Parent and children(PC) sets of each node obtained using the Max-min parent and children(MMPC) algorithm. Experimental results indicate that compared to other competitive methods, the proposed algorithm yields better solutions while maintaining high efficiency. Bayesian network(BN) sensitivity analysis is also proposed, which allows the network structure to be determined via a proposed ordering search method. We performed sensitivity analysis to determine the accuracy of the airborne avionics system, for which a simulation model is constructed to generate data samples, and the main effect of each error index is obtained using different sensitivity analysis methods. Experimental results indicate that the proposed BN method produces more accurate results when there is insufficient sample data, and this method can elucidate causal relationships that are present in the data.

关键词： Bayesian network Structure learning ordering search Sensitivity analysis Airborne avionics system

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Structure Learning of Bayesian Networks Based On the LARS-MMPC ordering search Method

Structure Learning of Bayesian Networks Based On the LARS-MM...

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第37届中国控制会议

作者： Chu-chao He Xiao-guang Gao Department of System Engineering Northwestern Polytechnical University （NWPU）

A given ordering among variables can significantly improve the accuracy of learning in Bayesian network *** this study, we propose using a combined Least Angle Regression（LARS） and Max-Min Parent and Children（MMPC） algorithm based on known root nodes specified by domain experts in order to obtain the optimal ordering. First, with a fixed root node, a partial ordering is tailored from the entire ordering by using the LARS algorithm. A further sequence is then obtained by combining all the different partial orderings. Parent and children sets are detected among the remaining nodes by the MMPC algorithm. Finally, a complete ordering is derived from the sequence and the parent and children sets, and the optimal structure is learnt by the K2 algorithm based on the ordering. Experiments showed that compared with other competitive methods, the proposed algorithm performed well in terms of balancing the learning accuracy with time consumption.

关键词： Bayesian network least angle regression max-min parent and children ordering search structure learning

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