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a novel bayesian network structure learning algorithm based on maximal information coefficient
Zhang Yinghua; Hu Qiping; Zhang Wensheng; Liu Jin
2012
Conference Name2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
Source2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
Pages862-867
Conference DateOctober 18, 2012 - October 20, 2012
Conference PlaceNanjing, China
Indexed TypeEI
ISBN9781467317436
Department(1) State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences BeiJing 100190 China; (2) International School of Software Wuhan University Wuhan 430074 China; (3) State Key Laboratory of Software Engineering Computer School Wuhan University Wuhan 430074 China
English AbstractGreedy Equivalent Search (GES) is an effective algorithm for Bayesian network problem, which searches in the space of graph equivalence classes. However, original GES may easily fall into local optimization trap because of empty initial structure. In this paper, An improved GES method is prosposed. It firstly makes a draft of the real network, based on Maximum Information Coefficient (MIC) and conditional independence tests. After this step, many independent relations can be found. To ensure correctness, then this draft is used to be a seed structure of original GES algorithm. Numerical experiment on four standard networks shows that NEtoGS (the number of graph structure, which is equivalent to the God Standard network) has big improvement. Also, the total of learning time are greatly reduced. Therefore, our improved method can relatively quickly determine the structure graph with highest degree of data matching. © 2012 IEEE.; Greedy Equivalent Search (GES) is an effective algorithm for Bayesian network problem, which searches in the space of graph equivalence classes. However, original GES may easily fall into local optimization trap because of empty initial structure. In this paper, An improved GES method is prosposed. It firstly makes a draft of the real network, based on Maximum Information Coefficient (MIC) and conditional independence tests. After this step, many independent relations can be found. To ensure correctness, then this draft is used to be a seed structure of original GES algorithm. Numerical experiment on four standard networks shows that NEtoGS (the number of graph structure, which is equivalent to the God Standard network) has big improvement. Also, the total of learning time are greatly reduced. Therefore, our improved method can relatively quickly determine the structure graph with highest degree of data matching. © 2012 IEEE.
KeywordArtificial Intelligence Equivalence Classes Learning Algorithms
SponsorshipIEEE Nanjing Section
Language英语
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/15944
Collection中国科学院软件研究所
Recommended Citation
GB/T 7714
Zhang Yinghua,Hu Qiping,Zhang Wensheng,et al. a novel bayesian network structure learning algorithm based on maximal information coefficient[C],2012:862-867.
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