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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 Name | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
| Source | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
| Pages | 862-867 |
| Conference Date | October 18, 2012 - October 20, 2012 |
| Conference Place | Nanjing, China |
| Indexed Type | EI |
| ISBN | 9781467317436 |
| 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 Abstract | 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.; 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. |
| Keyword | Artificial Intelligence Equivalence Classes Learning Algorithms |
| Sponsorship | IEEE Nanjing Section |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://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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