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Learning Bayesian network structure with immune algorithm

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Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This pa-per proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Further-more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Final y, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

structure learningBayesian networkimmune algo-rithmlocal optimal structurevaccination

Zhiqiang Cai、Shubin Si、Shudong Sun、Hongyan Dui

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Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, School of Mechatronics, Northwestern Polytechnical University, Xi’an 710072, China

This work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of ChinaProgram for New Century Excellent Talents in UniversityBasic Research Founda-tion of NPU

7110111671271170NCET-13-0475JC20120228

2015

10.1109/JSEE.2015.00034

系统工程与电子技术(英文版)
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会 中国系统仿真学会

系统工程与电子技术(英文版)

CSCDSCIEI
影响因子：0.64
ISSN：1004-4132
年,卷(期)：2015.(2)
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