计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 570-575.doi: 10.11896/jsjkx.201200038

• 信息安全 • 上一篇    下一篇

基于主成分回归和分层置信规则库的企业风险评估模型

刘栅杉1, 朱海龙1, 韩晓霞2, 穆全起1, 贺维1,2   

  1. 1 哈尔滨师范大学计算机科学与信息工程学院 哈尔滨150025
    2 中国人民解放军火箭军工程大学 西安710025
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 朱海龙(2656972775@qq.com)
  • 作者简介:1242185650@qq.com
  • 基金资助:
    黑龙江省自然科学基金项目(F2018023,LH2021F038);哈尔滨师范大学博士科研启动金项目(XKB201905);哈尔滨师范大学计算机科学与信息工程学院自然科学基金项目(JKYKYZ2020004)

Enterprise Risk Assessment Model Based on Principal Component Regression and HierarchicalBelief Rule Base

LIU Shan-shan1, ZHU Hai-long1, HAN Xiao-xia2, MU Quan-qi1, HE Wei1,2   

  1. 1 College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025,China
    2 Rocket Force University of Engineering,Xi'an 710025,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:LIU Shan-shan,born in 1997,postgra-duate.Her main research interests include artificial intelligence and belief rule base.
  • Supported by:
    Natural Science Foundation of Heilongjiang Province of China(F2018023,LH2021F038),Ph.D.Research Start-up Foundation of Harbin Normal University(XKB201905) and Natural Science Foundation of the School of Computer Science and Information Engineering,Harbin Normal University(JKYKYZ2020004).

摘要: 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。

关键词: 分层置信规则库, 企业风险评估, 投影协方差矩阵自适应进化策略, 证据推理, 主成分回归

Abstract: As a new intelligent expert system with the characteristics of expert system and data-driven model,the belief rule base (BRB) plays an important role in risk assessment and health status assessment.BRB has the advantages of processing numerical data and linguistic qualitative knowledge from heterogeneous sources,which can help enterprises conduct effective risk assessments.However,in the actual enterprise risk evaluation system,there are many types of indicators and redundancy.Traditional BRB cannot select indicators and is easy to cause rule explosion,which leads to problems such as large calculation amount and low model accuracy.In response to the above problems,this paper proposes a principal component regression and hierarchical confidence rule base (Principal Component Regression,Hierarchical Belief Rule Base,PCR-HBRB) enterprise risk assessment model,which saves calculation time by screening effective indicators,and combining qualitative with quantitative information to analyze and evaluate,to obtain higher accuracy evaluation results.Firstly,the PCR method is used to screen out the main indicators that affect the states of the company,the hierarchical confidence rule base (HBRB) inference model of company status risk assessment is established based on the selected indicators,and the model is reasoned by the evidence reasoning (ER).Then,the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used to optimize the model.Finally,the effectiveness of the model is verified through a risk assessment case of a certain enterprise's financial situation.

Key words: Enterprise risk assessment, Evidential reasoning, Hierarchical belief rule base, Principal component regression, Projection covariance matrix adaptation evolutionary strategies

中图分类号: 

  • TP391
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