Institutional Repository
| a novel bayesian network structure learning algorithm based on maximal information coefficient | |
| Zhang Yinghua; Hu Qiping; Zhang Wensheng; Liu Jin | |
| 2012 | |
| 会议名称 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
| 会议录名称 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
| 页码 | 862-867 |
| 会议日期 | October 18, 2012 - October 20, 2012 |
| 会议地点 | Nanjing, China |
| 收录类别 | EI |
| ISBN | 9781467317436 |
| 部门归属 | (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 |
| 摘要 | 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. |
| 关键词 | Artificial Intelligence Equivalence Classes Learning Algorithms |
| 主办者 | IEEE Nanjing Section |
| 语种 | 英语 |
| 内容类型 | 会议论文 |
| URI标识 | http://ir.iscas.ac.cn/handle/311060/15944 |
| 专题 | 中国科学院软件研究所 |
| 推荐引用方式 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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论