[1]LI-Binghan,GAO-Xiaoli,LIU-Sanyang,et al.Learning Bayesian network structures based on mutual information[J].CAAI Transactions on Intelligent Systems,2011,6(1):68-72.
Copy

Learning Bayesian network structures based on mutual information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 1 Page number: 68-72 Column: 学术论文—智能系统 Public date: 2011-02-25
Title:
Learning Bayesian network structures based on mutual information
Author(s):
LI-Binghan1;  GAO-Xiaoli1;  LIU-Sanyang1;  LI-Zhanguo2
1.Department of Mathematics, Xidian University, Xi’an 710071, China;
2.Department of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Keywords:
Bayesian network;  structure learning;  mutual information;  independence test;  maximum spanning tree
CLC:
TP181
DOI:
-
Abstract:
Constructing Bayesian network structures from data is an NPhard problem, and an improved algorithm was proposed based on mutual information. This algorithm built the initial skeleton using mutual information, refined the initial skeleton by employing the maximum spanning tree algorithm, and then oriented edges according to conditional independence tests. Finally, the optimal network structure was obtained using a greedy search. Numerical experiments show that both the BIC score and structural error made some improvements from previous results, and the number of iterations and running time was greatly reduced. Therefore the structure with highest degree of data matching was shown to be relatively faster as determined by the improved algorithm.
References:
［1］HANSEN J F. The clinical diagnosis of ischaemic heart disease due to coronary artery disease［J］. Danish Medical Bulletin, 1980, 27: 280286.
［2］WOLBRECHT E, AMBROSIO B D, PASSCH B, et al. Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks［J］. Artificial Intelligence for Engineering Design, Analysis & Manufacturing, 2000, 14(1): 5367.
［3］BEISER J A, RIGDON S E. Bayes prediction for the number of failures of a repairable system［J］. IEEE Transactions on Reliability, 1997, 46(2): 320326.
［4］PEARL J. Graphical models for probabilistic and causal reasoning［M］//TUCKER A B. The computer science and engineering handbook. Boca Raton, USA: CRC Press, 1997: 697714.
［5］SCHACHTER R D. Probabilistic inference and influence diagrams［J］. Operations Research, 1988, 36(4): 589605.
［6］MEEK C. Causal inference and causal explanation with background knowledge［C］//Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1995: 403410.
［7］BROMBERG F, MARGARITIS D, HONAVAR V. Efficient Markov network structure discovery using independence tests［J］. Journal of Artificial Intelligence Research, 2009, 35(1): 449485.
［8］MILAN S, JIRI V. A reconstruction algorithm for the essential graph［J］. International Journal of Approximate Reasoning, 2009, 50: 385413.
［9］冀俊忠,张鸿勋,胡仁兵,等.一种基于独立性测试和蚁群优化的贝叶斯网学习算法［J］.自动化学报, 2009, 35(3): 281288.
JI Junzhong, ZHANG Hongxun, HU Renbing, et al. A Bayesian network learning algorithm based on independence test and ant colony optimization［J］. Acta Automatica Sinica, 2009, 35(3): 281288.
?［10］PEDRO C P, ANDEAS N, MATHAUS D, et al. Using a local discovery ant algorithm for Bayesian network structure learning［J］. Transactions on Evolutionary Computation, 2009, 13(4): 767779.
［11］CHEN Xuewen, GOPALAKRISHNA A, LIN Xiaotong. Improving Bayesian network structure learning with mutual informationbased node ordering in the k2 algorithm［J］. IEEE Transactions on Knowledge and Data Engineering, 2008, 20: 113.
［12］LEVINE J, DUCATELLE F. Ant colony optimization and local search for bin packing and cutting stock problems［J］. Journal of the Operational Research Society, 2004, 55(7): 705716.
［13］LOBONA B, AFIF M, FAIEZ G, et al. Imporving algorithms for structure learning in Bayesian networks using a new implicit score［J］. Expert System with Applixation, 2010, 37: 54705475.
［14］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.
［15］CHICKERING D M. Learning equivalence classes of Bayesian network structures［J］. Journal of Machine Learning Research, 2002(2): 445498.
［16］RICHARD E N. Learning Bayesian networks［M］. Chicago, USA: Prentice Hall, 2002: 4043.
［17］PROAKIS J. Digital communications［M］. Columbus, USA: McGrawHill, 2000: 6190.
［18］CHICKERING D M. A transformational characterization of equivalent Bayesian network structures［C］//Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1995: 8798.
Similar References:

Memo

-

Last Update: 2011-04-13

Copyright © CAAI Transactions on Intelligent Systems