面向最佳决策结构的置信规则库结构学习方法

Structure Learning Approach of Belief Rule Base for Best Decision Structure

针对置信规则库中初始结构不合理的问题,现有的解决方法仍存在不具备可重复性或受数据完备性和等级效用值相关联的制约等方面的不足。鉴于此,对置信规则库的参数学习进行了理论分析和实验验证,总结出不合理结构下置信规则库中易出现结构欠完备问题或结构过完备问题;将DBSCAN算法和误差分析嵌入到现有参数学习方法中用于解决上述问题,进而提出了面向最佳决策结构的结构学习方法;通过实验分别在过完备结构和欠完备结构的置信规则库下验证了新方法,并对比了结构改变时误差的变化。实验结果表明所提方法是有效可行的。

For the problem of irrational initial structure of belief rule base （BRB）, the existing solving approaches still have deficiencies in many aspects such as non-repeatability, the completeness of data and the constraint with the associated level utility. In view of this, through theoretical analysis and experimental verification for parameter learning approaches of BRB, this paper summarizes that the irrational structure of BRB may lead to the problem of over-complete or incomplete structure. This paper takes the application of DBSCAN algorithm and error analysis to the existing parameter learning methods, and brings forth the structure learning approach for best decision structure. The experiments verify the new approach under over-complete and incomplete structures of BRB, and make a comparative analysis of the changes of error when the structure is varying. The results show the feasibility and effectiveness of the proposed approach.