基于马尔科夫毯约束的最优贝叶斯网络结构学习算法

Xiang Yuan Tan, Xiao Guang Gao, Chu Chao He

科研成果: 期刊稿件文章同行评审

6 引用 (Scopus)

摘要

To solve the problem about structure learning of optimal Bayesian network, this paper proposes Dynamic Programming Constrained with Markov Blanket (DPCMB), which uses Markov Blanket calculated by Incremental Association Markov Blanket (IAMB) to reduce the number of scoring calculations in Dynamic Programming. We research on the effect of the significance value in IAMB on the performance indicators of DPCMB algorithm, and give reasonable suggestions for adjusting the significance value. Experimental results show that the DPCMB algorithm can adjust the significance value so that the accuracy of the algorithm is comparable to that of the DP algorithm, and running time, score calculation times, and memory requirements of the algorithm are greatly reduced.

投稿的翻译标题Learning Optimal Bayesian Network Structure Constrained with Markov Blanket
源语言繁体中文
页(从-至)1898-1904
页数7
期刊Tien Tzu Hsueh Pao/Acta Electronica Sinica
47
9
DOI
出版状态已出版 - 1 9月 2019

关键词

  • Bayesian network structure learning
  • Dynamic programming
  • IAMB
  • Markov blanket

指纹

探究 '基于马尔科夫毯约束的最优贝叶斯网络结构学习算法' 的科研主题。它们共同构成独一无二的指纹。

引用此