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Personal credit scoring model of non-linear combining forecast based on GP

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Aiming at low predictive accuracies of single models, this paper presents a combining forecast for personal credit scoring. Based on two single statistical models of linear regression and logistic regression, this paper constructed a non-linear combining forecast by genetic programming (GP) and used the constructed model for personal credit scoring. The application results indicate that the predictive accuracy of the non-linear combining forecast based on GP is higher than linear regression, logistic regression and the linear combining forecast based on least square method by 3.40%, 2.83% and 2.64% respectively. The non-linear combining forecast also gets a much lower type II error rate which is more significant for commercial banks to keep away from consumer credit risks.

Original languageEnglish
Title of host publicationProceedings - Third International Conference on Natural Computation, ICNC 2007
Pages408-414
Number of pages7
DOIs
StatePublished - 2007
Externally publishedYes
Event3rd International Conference on Natural Computation, ICNC 2007 - Haikou, Hainan, China
Duration: 24 Aug 200727 Aug 2007

Publication series

NameProceedings - Third International Conference on Natural Computation, ICNC 2007
Volume4

Conference

Conference3rd International Conference on Natural Computation, ICNC 2007
Country/TerritoryChina
CityHaikou, Hainan
Period24/08/0727/08/07

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