HE Chuchao, GAO Xiaoguang, WAN Kaifang, “MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 147-153, 2020, doi: 10.1049/cje.2019.11.004
Citation: HE Chuchao, GAO Xiaoguang, WAN Kaifang, “MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 147-153, 2020, doi: 10.1049/cje.2019.11.004

MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application

doi: 10.1049/cje.2019.11.004
Funds:  This work is supported by the National Natural Science Foundation of China (No.61573285).
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  • Corresponding author: WAN Kaifang (corresponding author) was born in 1987. He was awarded with Ph.D. in System Engineering in 2016. His current research interests include sensor management application, multi-agent theory, approximate dynamic programming and reinforcement learning theory. (Email:wankaifang@nwpu.edu.cn)
  • Received Date: 2018-11-30
  • Rev Recd Date: 2019-09-18
  • Publish Date: 2020-01-10
  • 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.
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