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Volume 42 Issue 7
Jul.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(7): 1486-1493.
SHEN Lin, YU Jinsong, TANG Diyin, et al. Automatic learning of Bayesian network structure using graph model and learning algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(7): 1486-1493. doi: 10.13700/j.bh.1001-5965.2015.0445(in Chinese)
Citation: SHEN Lin, YU Jinsong, TANG Diyin, et al. Automatic learning of Bayesian network structure using graph model and learning algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(7): 1486-1493. doi: 10.13700/j.bh.1001-5965.2015.0445(in Chinese)
shu

Automatic learning of Bayesian network structure using graph model and learning algorithm

doi: 10.13700/j.bh.1001-5965.2015.0445
  • 1.

    School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. Collaborative Innovation Center of Advanced Aero-Engine, Beijing University of Aeronautics and Astronautics, Beijing 10008

  • 3. Unit 93, Army 95809 of PLA, Cangzhou 061736, China

  • Received Date: 02 Jul 2015
  • Publish Date: 20 Jul 2016
    Abstract
  • In order to improve the accuracy and efficiency of the data-driven approaches in learning Bayesian network structure, expert knowledge is usually implemented in the learning algorithm. To deal with the lack of effective ways to combine the expert knowledge and the data-driven learning approaches in the existing methods, this paper proposes an automatic learning method for Bayesian network structure learning, which combines multi-signal flow graphs and learning algorithm K2. The method inserts expert knowledge into data-driven learning methods, using the information of relationships between signals from multi-signal flow graphs and the structure learning algorithm K2, to achieve automatic learning of Bayesian network structure. Numerical analysis, compared with other typical network structure learning algorithms, proves that the proposed method significantly lowers the structure learning requirements for learning scale and training data size and provides a higher learning accuracy and computation efficiency. The application of the proposed method is illustrated using a real engineering system and verified the practicability of the algorithm at the same time.

     

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