小数据集情况下基于变权重融合的BN参数学习算法

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (05): 916-923.

小数据集情况下基于变权重融合的BN参数学习算法

郭文强1,2,寇馨1，李梦然3，侯勇严3，肖秦琨4

(1.陕西科技大学电子信息与人工智能学院，陕西西安 710021；2.陕西科技大学陕西省人工智能联合实验室，陕西西安 710021；
3.陕西科技大学电气与控制工程学院，陕西西安 710021；4.西安工业大学电子信息工程学院，陕西西安 710021)

收稿日期:2020-10-10 修回日期:2020-12-17 接受日期:2022-05-25 出版日期:2022-05-25 发布日期:2022-05-24
基金资助:
国家自然科学基金(62071366,61271363);陕西省科技厅重点研发计划(2020SF-286);陕西省教育厅产业化研究项目(18JC003);西安市科技计划(2019216514GXRC001CG002GXYD1.1)

A BN parameter learning algorithm based on variable weight fusion for small datasets

GUO Wen-qiang1,2,KOU Xin1,LI Meng-ran3,HOU Yong-yan3,XIAO Qin-kun4

(1.School of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021；
2.Shaanxi Joint Laboratory of Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021；
3.School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi’an 710021；
4.School of Electronic Information Engineering,Xi’an University of Technology,Xi’an 710021,China)

Received:2020-10-10 Revised:2020-12-17 Accepted:2022-05-25 Online:2022-05-25 Published:2022-05-24

摘要/Abstract

摘要： 针对小数据集情况下贝叶斯网络（BN）参数学习结果精度较低的问题，分析了小数据集情况下BN参数变权重设计的必要性，提出一种基于变权重融合的BN参数学习算法VWPL。首先根据专家经验确定不等式约束条件，计算参数学习最小样本数据集阈值，设计了随样本量变化的变权重因子函数；然后根据样本计算出初始参数集，通过Bootstrap方法进行参数扩展得到满足约束条件的候选参数集，将其代入BN变权重参数计算模型即可获取最终的BN参数。实验结果表明，当学习数据量较小时，VWPL算法的学习精度高于MLE算法和QMAP算法的，也优于定权重学习算法的。另外，将VWPL算法成功应用到了轴承故障诊断实验中，为在小数据集上进行BN参数估计提供了一种方法。

关键词: 贝叶斯网络, 小数据集, 变权重融合, 参数学习

Abstract: Aiming at the problem of low accuracy of Bayesian network (BN) parameter learning results under the condition of small datasets, the necessity of variable weight design of BN parameters under the condition of small datasets is analyzed, and a BN parameter learning algorithm based on variable weight fusion, named VWPL, is proposed. Firstly, the inequality constraints are determined according to the expert experience, the minimum sample data set threshold is calculated and the variable weight factor function that changes with the sample size is designed. Then, the initial parameter set is calculated according to the sample, and the parameter expansion is carried out by the Bootstrap method to obtain the candidate parameter set that meets the constraints, and the final BN parameters can be obtained by substituting them into the BN variable weight parameter calculation model. The experimental results show that when the amount of learning data is small, the learning accuracy of the VWPL algorithm is higher than that of the MLE algorithm, the QMAP algorithm and the fixed-weight learning algorithm. In addition, the VWPL algorithm is successfully applied to the bearing fault diagnosis experiment, which provides a method of BN parameter estimation for small datasets.

Key words: Bayesian network, small dataset, variable weight fusion, parameter learning

郭文强, 寇馨, 李梦然, 侯勇严, 肖秦琨. 小数据集情况下基于变权重融合的BN参数学习算法[J]. 计算机工程与科学, 2022, 44(05): 916-923.

GUO Wen-qiang, KOU Xin, LI Meng-ran, HOU Yong-yan, XIAO Qin-kun . A BN parameter learning algorithm based on variable weight fusion for small datasets[J]. Computer Engineering & Science, 2022, 44(05): 916-923.

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