MMOS+ ordering search method for Bayesian network structure learning and its application

Chuchao He, Xiaoguang Gao, Kaifang Wan

科研成果: 期刊稿件文章同行评审

5 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)147-153
页数7
期刊Chinese Journal of Electronics
29
1
DOI
出版状态已出版 - 2020

指纹

探究 'MMOS+ ordering search method for Bayesian network structure learning and its application' 的科研主题。它们共同构成独一无二的指纹。

引用此