a novel bayesian network structure learning algorithm based on maximal information coefficient
文献类型：会议论文
| 作者 | Zhang Yinghua ; Hu Qiping ; Zhang Wensheng ; Liu Jin |
| 出版日期 | 2012 |
| 会议名称 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
| 会议日期 | October 18, 2012 - October 20, 2012 |
| 会议地点 | Nanjing, China |
| 关键词 | Artificial intelligence Equivalence classes Learning algorithms |
| 页码 | 862-867 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | IEEE Nanjing Section |
| 会议录 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
 |
| 语种 | 英语 |
| ISBN号 | 9781467317436 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15944]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012. Nanjing, China. October 18, 2012 - October 20, 2012. |
入库方式： OAI收割
来源：软件研究所
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