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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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    • References(21)
  • [1]
    JENSEN F V.Bayesian networks and decision graphs[M].New York:Springer,2001:32-34.
    [2]
    EFRON B.Bayes' theorem in the 21st century[J].Science,2013,340(6137):1177-1178.
    [3]
    AOUAY S,JAMOUSSI S,BEN AYED Y.Particle swarm optimization based method for Bayesian network structure learning[C]//2013 5th International Conference on Modeling,Simulation and Applied Optimization (ICMSAO).Piscataway,NJ:IEEE Press,2013:1-6.
    [4]
    张振海,王晓明,党建武,等.基于专家知识融合的贝叶斯网络结构学习方法[J].计算机工程与应用,2014,50(2):1-4.ZHANG Z H,WANG X M,DANG J W,et al.Bayesian network structure learning method based on expert knowledge fusion[J].Computer Engineering and Applications,2014,50(2):1-4(in Chinese).
    [5]
    BALABAN E,SAXENA A,NARASIMHAN S,et al.Prognostic health-management system development for electromechanical actuators[J].Journal of Aerospace Information Systems,2015,12(3):329-344.
    [6]
    代京,于劲松,张平,等.基于多信号流图的诊断贝叶斯网络建模[J].北京航空航天大学学报,2009,35(4):472-475.DAI J,YU J S,ZHANG P,et al.Diagnostic Bayesian networks modeling based on multi-signal flow graphs[J].Journal of Beijing University of Aeronautics and Astronautics,2009,35(4):472-475(in Chinese).
    [7]
    陈希祥,邱静,刘冠军.测试不确定条件下基于贝叶斯网络的装备测试优化选择技术[J].中国机械工程,2011,22(4):379-384.CHEN X X,QIU J,LIU G J.Test selection of materiel based on Bayesian network under test uncertainty[J].China Mechanical Engineering,2011,22(4):379-384(in Chinese).
    [8]
    Qualtech Systems Inc.Testability,engineering and maintenance system(TEAMS)[EB/OL].[2010-07-26]. http://www.teamqsi.com/products/teams-designer/.
    [9]
    吴红,王维平,杨峰.融合先验信息的贝叶斯网络结构学习方法[J].系统工程与电子技术,2012,34(12):2585-2591.WU H,WANG W P,YANG F.Structure learning method of Bayesian network with prior information [J].Systems Engineering and Electronics,2012,34(12):2585-2591(in Chinese).
    [10]
    MASEGOSA A R,MORAL S.New skeleton-based approaches for Bayesian structure learning of Bayesian networks[J].Applied Soft Computing,2013,13(2):1110-1120.
    [11]
    BOUCHAALA L,MASMOUDI A,GARGOURI F,et al.Improving algorithms for structure learning in Bayesian networks using a new implicit score[J].Expert Systems with Applications,2010,37(7):5470-5475.
    [12]
    COOPER G F,HERSKOVITS E. A Bayesian method for the induction of probabilistic networks from data[J].Machine Learning,1992,9(4):309-347.
    [13]
    CHENG J,GREINER R,KELLY J,et al.Learning Bayesian networks from data: An information-theory based approach[J].Artificial Intelligence,2002,137(12):43-90.
    [14]
    SILANDER T,ROOS T,MYLLYMÄKI P.Learning locally minimax optimal Bayesian networks[J].International Journal of Approximate Reasoning,2010,51(5):544-557.
    [15]
    WELLMAN M P,BREESE J S,GOLDMAN R P.From knowledge bases to decision models[J].The Knowledge Engineering Review,1992,7(1):35-53.
    [16]
    DEB S,PATTIPATI K R,SHRESTHA R.QSI's integrated diagnostics toolset[C]//1997 IEEE Autotestcon Proceedings AUTOTESTCON,97.Piscataway,NJ:IEEE Press,1997:408-421.
    [17]
    YU K,WANG H,WU X.A parallel algorithm for learning Bayesian networks[M]//Advances in Knowledge Discovery and Data Mining. Berlin Heidelberg:Springer,2007:1055-1063.
    [18]
    曾安,李晓兵,杨海东,等.基于最小描述长度和K2的贝叶斯网络结构学习算法[J].东北师大学报(自然科学版),2014,46(3):53-58.ZENG A,LI X B,YANG H D,et al.Bayesian network structure learning based on minimum description length and K2 algorithm[J].Journal of Northeast Normal University(Natural Science Edition),2014,46(3):53-58(in Chinese).
    [19]
    吴永广,庞世春.K2 & HC结构学习算法[J].计算机与数字工程,2014,42(7):1137-1140.WU Y G,PANG S C.K2 & HC structure learning algorithm[J].Computer & Digital Engineering,2014,42(7):1137-1140(in Chinese).
    [20]
    TIAN J,PEARL J.Causal discovery from changes[C]//Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc.,2001:512-521.
    [21]
    TSAMARDINOS I,BROWN L E,ALIFERIS C F.The max-min hill-climbing Bayesian network structure learning algorithm[J].Machine Learning,2006,65(1):31-78.
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