Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 2
390-394

Extension parameters learning for BN based on constraints and maximum entropy model

Guo Wenqiang
Li Ran
Hou Yongyan
Gao Wenqiang
School of Electrical & Information Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
DOI: 10.19734/j.issn.1001-3695.2017.08.0868

Abstract

While the intelligent systems need parameter modeling, users often face the dilemma of scarce modeling samples. This paper proposed a BN parameter learning method-constrained data maximum entropy(CDME) algorithm for the modeling of BN parameters under the small data sets. In the case of estimating BN parameters by using small data sets, it transformed the qualitative expert knowledge into inequality constraints for the sake of generating candidate parameter sets by Bootstrap algorithm. Then it estimated the BN parameters in the light of the maximum entropy principle. The experimental results show that CDME algorithm learning effects are similar to the classical MLE algorithm when the modeling data size is sufficient. However, when the data size is limited, the parameters of BN can be modeled by using the CDME, and the learnt accuracy is superior to MLE or QMAP algorithm. It also applied CDME to a real fault diagnosis while the data set was relatively scarce. The results of the diagnosis reasoning demonstrate that the presented parameter learning approach is effective. The CDME parameter learning algorithm provides a new modeling way for BN parameter under the small data sets.

Foundation Support

陕西省科技厅自然科学基金资助项目(2017JM6057)
陕西省教育厅专项自然科学基金资助项目(2013JK1114)
陕西省教育厅2018年度服务地方科学研究计划项目(18JC003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.08.0868
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 2
Section: Algorithm Research & Explore
Pages: 390-394
Serial Number: 1001-3695(2019)02-017-0390-05

Publish History

[2019-02-05] Printed Article

Cite This Article

郭文强, 李然, 侯勇严, 等. 约束条件下BN参数最大熵模型扩展学习算法 [J]. 计算机应用研究, 2019, 36 (2): 390-394. (Guo Wenqiang, Li Ran, Hou Yongyan, et al. Extension parameters learning for BN based on constraints and maximum entropy model [J]. Application Research of Computers, 2019, 36 (2): 390-394. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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