HE Chuchao, GAO Xiaoguang, WAN Kaifang. MMOS+ Ordering Search Method for Bayesian Network Structure Learning and Its Application[J]. Chinese Journal of Electronics, 2020, 29(1): 147-153. 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[J]. Chinese Journal of Electronics, 2020, 29(1): 147-153. 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).
More Information
  • 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.
  • loading
  • J. Pearl, "Probabilistic reasoning in intelligent systems", Jouranl of Philosophy, Vol.88, No.8, pp.434-437, 1988.
    J. Y. Wang, S. Y. Liu and M. M. Zhu, "Structure learning of chain graphs using the conditional independence tests", Acta Electronica Sinica, Vol.45, No.10, pp.133-138, 2017. (in Chinese)
    M. Teyssier and D. Koller, "Ordering-based search:a simple and effective algorithm for learning bayesian networks", Conference on Uncertainty in Artificial Intelligence, Catalina Island, USA, pp.584-590, 2012.
    I. Tsamardinos, C.F. Aliferis and A. Statnikov, "Time and sample efficient discovery of markov blankets and direct causal relations", in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D.C., USA, pp.673-678, 2003.
    P. Larranaga, C. M. H. Kuijpers, R. H. Murga, et al., "Learning bayesian network structures by searching for the best ordering with genetic algorithms", IEEE Transactions on Systems, Man & Cybernetics, Part A, (Systems & Humans), Vol.26, No.4, pp.487-493, 1996.
    G. F. Cooper and E. Herskovits, "A bayesian method for the induction of probabilistic networks from data", Machine Learning, Vol.9, No.4, pp.309-347, 1992.
    N. Friedman and D. Koller, "Being bayesian about network structure:A Bayesian approach to structure discovery in bayesian networks", Machine Learning, Vol.50, No.1-2, pp.95-125, 2003.
    F. Liu, F.Z. Tian and Q.L. Zhu, "A novel ordering-based greedy bayesian network learning algorithm on limited data", Springer Berlin Heidelberg, Berlin, Germany, pp.80-89, 2007.
    C.C. He and X.G. Gao, "Strucutre learning on Bayesian networks by finding the optimal ordering with and without priors", Journal of Systems Engineering and Electronics, Vol.29, No.6, pp.1029-1227, 2018.
    S.T. Chiu, "Regression analysis:Theory, methods, and applications", Journal of the American Statistical Association, Vol.33, No.4, pp.479-380, 1990.
    R.I. Cukier, C.M. Fortuin, K.E. Shuler, et al., "Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients", Journal of Chemical Physics, Vol.59, No.8, pp.3873-3878, 1973.
    C.M. Anderson-Cook, "Responce surface methodology:Process and product optimization using designed experiments by raymond h. myers; douglas c. montgomery", Journal of the American Statistical Association, Vol.97, No.460, pp.293-300, 2002.
    G.C. Critchfield and K.E. Willard, "Probabilistic analysis of decision trees using monte carlo simulation", Medical Decision Making, Vol.6, No.2, pp.85-92, 1986.
    B.I.M. Sobol, "On sensitivity estimates for nonlinear mathematical models", Keldysh Applied Mathematics Institute, Vol.2, No.1, pp.112-118, 1910.
    . O. Agnieszka, J. Marek and Druzdzel. "Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems", Artificial Intelligence in Medicine, Vol.57, No.3, pp.197-206, 2013.
    C.C. He and X.G. Gao, "Sensitivity analysis on accuracy of helicopter fire control system:A bnsobol method", Acta Aeronautica Et Astronautica Sinica, Vol.37, No.10, pp.3110-3120, 2016. (in Chinese)
    I. Tsamardinos, L. E. Brown and C. F. Aliferis, "The max-min hill-climbing bayesian network structure learning algorithm", Machine Learning, Vol.65, No.1, pp.31-78, 2006.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (110) PDF downloads(563) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint