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

Authors

Zhenhai Wan, College of Computer Science, Inner Mongolia University, Hohhot 010021, China;
Tieying Liu, College of Computer Science, Inner Mongolia University, Hohhot 010021, China;
Zhang Yang, College of Computer Science, Inner Mongolia University, Hohhot 010021, China;
Jishuang Li, College of Computer Science, Inner Mongolia University, Hohhot 010021, China;

Abstract

Abstract: 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.

Recommended Citation

Wan, Zhenhai; Liu, Tieying; Yang, Zhang; and Li, Jishuang (2020) "Simulation and Application of DKIPSO-SVC Combined Model for Credit Risk Assessment," Journal of System Simulation: Vol. 27: Iss. 8, Article 32.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss8/32

First Page

1875

Revised Date

2015-07-19

DOI Link

https://doi.org/

Last Page

1880

CLC

TP391.9;F830

Recommended Citation

Wan Zhenhai, Liu Tieying, Zhang Yang, Li Jishuang. Simulation and Application of DKIPSO-SVC Combined Model for Credit Risk Assessment[J]. Journal of System Simulation, 2015, 27(8): 1875-1880.