A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

基于主成分回归和分层置信规则库的企业风险评估模型

作为一种具有专家系统和数据驱动模型特征的新型智能专家系统,置信规则库(Belief Rule Base,BRB)在风险评估和健康状态评估等领域中发挥着重要作用。BRB因其自身既可以处理数值数据,又可以处理来自异构源的语言定性知识的优势,能够帮助企业进行有效的风险评估。但是实际企业风险评估体系中指标种类较多且有冗余性,传统BRB无法进行指标选择且易造成规则爆炸从而导致计算量大和模型准确度较低等问题。针对上述问题,文中提出一种主成分回归和分层置信规则库(Principal Component Regression,Hierarchical Belief Rule Base,PCR-HBRB)的企业风险评估模型,通过筛选有效指标节约计算时间,同时结合定性与定量信息进行分析评估从而得到较高准确度的评估结果。首先,通过PCR筛选出影响企业的主要指标,根据筛选出来的指标建立分层置信规则库(HBRB)的企业风险评估推理模型,并采用证据推理(Evidential Reasoning,ER)对模型进行推理。然后,采用投影协方差矩阵自适应进化策略(Projection Covariance Matrix Adaptation Evolutionary Strategies,P-CMA-ES)对模型进行优化。最后,通过对某企业的财务状况进行风险评估案例验证了模型的有效性。