基于聚类集成和激活因子的扩展置信规则库推理模型

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.