中国科学院软件研究所机构知识库
Advanced  
ISCAS OpenIR  > 软件所图书馆  > 会议论文
Title:
a novel bayesian network structure learning algorithm based on maximal information coefficient
Author: Zhang Yinghua ; Hu Qiping ; Zhang Wensheng ; Liu Jin
Source: 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
Conference Name: 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
Conference Date: October 18, 2012 - October 20, 2012
Issued Date: 2012
Conference Place: Nanjing, China
Keyword: Artificial intelligence ; Equivalence classes ; Learning algorithms
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
Sponsorship: IEEE Nanjing Section
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15944
Appears in Collections:软件所图书馆_会议论文

Files in This Item:

There are no files associated with this item.


Recommended Citation:
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.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zhang Yinghua]'s Articles
[Hu Qiping]'s Articles
[Zhang Wensheng]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zhang Yinghua]‘s Articles
[Hu Qiping]‘s Articles
[Zhang Wensheng]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2019  中国科学院软件研究所 - Feedback
Powered by CSpace