具有丢失数据的贝叶斯网络结构学习研究
DOI:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

Supported by the National Natural Science Foundation of China under Grant No.60275026(国家自然科学基金);the NaturalScience Foundation of Jilin Province of China under GrantNo.20030517-1(吉林省自然科学基金)


Research on Learning Bayesian Networks Structure with Missing Data
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    目前主要基于EM算法和打分-搜索方法进行具有丢失数据的贝叶斯网络结构学习,算法效率较低,而且易于陷入局部最优结构.针对这些问题,建立了一种新的具有丢失数据的贝叶斯网络结构学习方法.首先随机初始化未观察到的数据,得到完整的数据集,并利用完整数据集建立最大似然树作为初始贝叶斯网络结构,然后进行迭代学习.在每一次迭代中,结合贝叶斯网络结构和Gibbs sampling修正未观察到的数据,在新的完整数据集的基础上,基于变量之间的基本依赖关系和依赖分析思想调整贝叶斯网络结构,直到结构趋于稳定.该方法既解决了标准Gi

    Abstract:

    At present, the method of learning Bayesian network structure with missing data is mainly based on the search and scoring method combined with EM algorithm. The algorithm has low efficiency and easily gets into local optimal structure. In this paper, a new method of learning Bayesian network structure with missing data is presented. First, unobserved data are randomly initialized. As a result, a complete data set is got. Based on the complete data set, the maximum likelihood tree is built as an initialization Bayesian network structure. Second, unobserved data are reassigned by combining Bayesian network with Gibbs sampling. Third, on the basis of the new complete data set, the Bayesian network structure is regulated based on the basic dependency relationship between variables and dependency analysis method. Finally, the second and third steps are iterated until the structure goes stable. This method can avoide the exponential complexity of standard Gibbs sampling and the main problems in the existing algorithm. It provides an effective and applicable method for uncertain knowledge representation, inference, and reasoning with missing data.

    参考文献
    相似文献
    引证文献
引用本文

王双成,苑森淼.具有丢失数据的贝叶斯网络结构学习研究.软件学报,2004,15(7):1042-1048

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2003-01-20
  • 最后修改日期:2003-09-15
  • 录用日期:
  • 在线发布日期:
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京市海淀区中关村南四街4号,邮政编码:100190
电话:010-62562563 传真:010-62562533 Email:jos@iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号