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Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning

机译：贝叶斯网络结构学习的上下界候选集搜索算法

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摘要

Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a resu after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.
机译：贝叶斯网络是人工智能领域的重要理论模型，也是处理不确定性问题的有力工具。考虑到当前贝叶斯网络结构学习算法的收敛速度较慢，提出了一种快速的混合学习方法。我们从进一步分析低阶条件独立性测试提供的信息开始，然后给出两种构造网络图模型的方法，理论上证明这是目标网络结构空间的上限和下限，因此候选给出结果集；之后，基于候选集运行搜索和评分算法，以找到网络的最终结构。仿真结果表明，本文提出的算法比具有相同学习精度的同类算法效率更高。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第21期|625173.1-625173.6|共6页
  • 作者

    Liu Guangyi; Li Ou; Zhang Dalong; Song Tao;

  • 作者单位

    Zhengzhou Informat Sci & Technol Inst, Zhengzhou 450000, Peoples R China.;

    Zhengzhou Informat Sci & Technol Inst, Zhengzhou 450000, Peoples R China.;

    Zhengzhou Informat Sci & Technol Inst, Zhengzhou 450000, Peoples R China.;

    Zhengzhou Informat Sci & Technol Inst, Zhengzhou 450000, Peoples R China.;

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