信用风险评估中DKIPSO-SVC组合模型的仿真研究

Simulation and Application of DKIPSO-SVC Combined Model for Credit Risk Assessment

借助于支持向量分类机(SVC)的强泛化能力与鲁棒性,针对GDS-SVC、DIPSO-SVC选取参数的低效性,在改进的粒子群算法(DIPSO)位置更新过程中引入缩减因子(DKIPSO),建立基于DKIPSO自动选取SVC参数的DKIPSO-SVC组合模型,并将其应用于商业银行的信用评估。仿真结果表明,DKIPSO-SVC模型的鲁棒性优于DIPSO-SVC;DKIPSO-SVC分类精度为96.6049%,高于DIPSO-SVC93.8272%和GDS-SVC92.5926%。DKIPSO-SVC模型把第2类误判率从8.5526%降低到1.9737%,降低幅度近76.9228%,这将在极大程度上规避了商业银行的信用风险。

In order to improve the problem of inefficient parameter selection of the GDS-SVC model and DIPSO-SVC model, and utilize the generalization ability and robustness of support vector classification(SVC), the reduction factor of location updating was introduced based on the dynamic improvement Particle Swarm Optimization(DIPSO), and then the DKIPSO-SVC of parameters selecting in SVC was established based on DKIPSO. The method was applied to credit scoring of commercial banks. The simulation results demonstrate that the robustness of the DKIPSO-SVC model is better than DIPSO-SVC. But beyond that, the accuracy of DKIPSO-SVC model achieves 96.6049%, higher than that of DIPSO-SVC and GDS-SVC model, which is 93.8272% and 92.5926%. More importantly, the type II error rate was reduced significantly from 8.5526% to1.9737%, about 76.9228% lower than current model.