基于规则聚类和参数学习的扩展置信规则库推理模型

扩展置信规则库(EBRB)中的规则数量和参数取值共同影响EBRB推理模型的决策准确性和计算效率. 基于此,提出一种基于规则聚类和参数学习的改进EBRB推理模型,称为RCPL-EBRB模型.所提出模型的基本原理如下:首先,依据密度聚类分析对EBRB进行规则聚类来识别EBRB中无效的扩展置信规则和优化传统EBRB的建模过程;然后,以聚类所得到的规则簇(即Sub-EBRB)进行参数学习和规则推理,保证激活规则集合的一致性,从而提高RCPL-EBRB模型的决策准确性和计算效率;最后,引入非线性函数拟合和基准分类问题数据集开展模型的有效性检验和参数灵敏度分析.实验结果表明,所提出RCPL-EBRB模型比现有EBRB推理模型和传统机器学习方法具有更高的决策准确性.

Extended belief rule base inference model based on rule clustering and parameter learning

The number of rules and parameter values in extended belief rule base(EBRB) affect the accuracy and computing efficiency of the EBRB inference model. Therefore, this paper proposes an improved EBRB inference method based on rule clustering and parameter learning, called RCPL-EBRB model. The principles of the proposed model include: The density clustering analysis is firstly used to perform the rule clustering of the EBRB, so as to identify invalid extended belief rules and improve the modeling process of the traditional EBRB. Then, the rule clusters obtained by clustering, namely sub-EBRB, are used as basic units for parameter learning and rule reasoning, so as to improve the accuracy and computing efficiency of the RCPL-EBRB model. Finally, the datasets of nonlinear function fitting and benchmark classification problems are introduced to verify the effectiveness of the proposed model and carry out parameters sensitivity analysis. Results show that the RCPL-EBRB model has higher accuracy than the existing EBRB inference model and traditional machine learning methods.