基于聚类集成和激活因子的扩展置信规则库推理模型

规则约减和规则激活是扩展置信规则库(EBRB)推理模型优化研究中的两个重要方向.然而,现有研究成果大多存在方法参数确定主观性强和计算复杂度高等不足.为此,通过引入聚类集成和激活因子提出改进的EBRB推理模型,称为CEAF-EBRB模型.该模型先基于聚类集成对历史数据进行多次的数据聚类分析,再以簇为单位将所有历史数据生成扩展置信规则;同时,通过激活因子修正个体匹配度计算公式以及离线的方式计算激活因子取值,以确保高效地激活一致性的规则.最后,在非线性函数拟合、模式识别、医疗诊断等常见问题中验证了所提CEAF-EBRB模型的可行性和有效性,从而为决策者提供更准确的决策支持.

Extended belief rule base inference model based on clustering ensemble and activation factor

Rule reduction and rule activation are two important directions in the studies of improving extended belief rule base(EBRB) inference models. However, most of these studies are still suffering challenges, such as strong subjectivity of parameters determination and/or a high computational complexity. For this reason, this paper proposes an improved EBRB inference model, which is called CEAF-EBRB model, based on the clustering ensemble and activation factor. The CEAF-EBRB model performs multiple data clustering analyses on historical data based on the clustering ensemble firstly, and then generates extended belief rules from all historical data in the unit of clusters. Meanwhile, the activation factor is used to modify the calculation of individual matching degrees and then effectively activate consistent rules after using an offline way to initialize the activation factor. Finally, the feasibility and effectiveness of the CEAF-EBRB model are verified through solving non-linear function fitting, pattern recognition, and medical diagnosis. The proposed model can provide a more accurate decision support for decision-makers.