a novel bayesian network structure learning algorithm based on maximal information coefficient

CORC > 软件研究所 > 软件所图书馆 > 会议论文

	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
内容类型	会议论文
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.