小数据集条件下基于数据再利用的BN参数学习

杨宇; 高晓光; 郭志高

doi:10.16383/j.aas.2015.c140838

小数据集条件下基于数据再利用的BN参数学习

doi: 10.16383/j.aas.2015.c140838 cstr: 32138.14.j.aas.2015.c140838

1.
西北工业大学电子信息学院西安 710129

基金项目:

国家自然科学基金(60774064,61573285),教育部博士点基金(20116102110026)资助

详细信息

作者简介:
杨宇西北工业大学电子信息学院博士研究生. 主要研究方向为小数据集条件下贝叶斯网络结构和参数学习.E-mail: youngiv@126.com

通讯作者:
高晓光西北工业大学电子信息学院教授.主要研究方向为智能决策, 复杂系统建模与效能分析.本文通信作者.

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出版历程
- 收稿日期: 2014-12-04
- 修回日期: 2015-07-21
- 刊出日期: 2015-12-20

Learning BN Parameters with Small Data Sets Based by Data Reutilization

1.
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129

Funds:

Supported by National Natural Science Foundation of China (60774064, 61573285) and Research Fund for the Doctoral Program Higher Education of China (20116102110026)

摘要

摘要: 着重研究了小数据集条件下结合凸约束的离散贝叶斯网络(Bayesian network, BN)参数学习问题, 主要任务是用先验知识弥补数据的不足以提高参数学习精度. 已有成果认为数据和先验知识是独立的, 在参数学习算法中仅将二者机械结合. 经过理论研究后, 本文认为数据和先验知识并不独立, 原有算法浪费了这部分有用信息. 本文立足于数据信息分类, 深入挖掘数据和先验知识之间的约束信息来提高参数学习精度, 提出了新的BN 参数学习算法 --凸约束条件下基于数据再利用的贝叶斯估计. 通过仿真实验展示了所提算法在精度和其他性能上的优势, 进一步证明数据和先验知识不独立思想的合理性.
- 贝叶斯网络 /
- 参数学习 /
- 小数据集 /
- 数据信息分类 /
- 数据再利用
Abstract: In this paper, parameters learning of discrete Bayesian networks (BNs) with small data sets with convex constraints is investigated, and the main task is improving the accuracy of parameter learning through offsetting the lack of data with prior knowledge. Data and prior knowledge are often mechanically integrated in most existing algorithms because they are treated independent. However, after a theoretical study, they are found dependent on each other, and the existing algorithms have dissipated this relevance. A novel parameter learning algorithm --Bayesian estimation based on data reutilization under convex constraints, is proposed with deeply mining the information between data and prior knowledge based on classification of data information. Finally, simulations demonstrate the advantages of novel algorithm in precision and other indexes, which in turn tells the dependance between data and prior information.
- Bayesian network (BN) /
- parameter learning /
- small data sets /
- classification about data information /
- reutilization of data

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参考文献(24)

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