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Content Provider | IEEE Xplore Digital Library |
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Author | Ming-hui Jiang Xu-chuan Yuan |
Copyright Year | 2007 |
Description | Author affiliation: Harbin Inst. of Technol., Harbin (Ming-hui Jiang; Xu-chuan Yuan) |
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. |
Starting Page | 408 |
Ending Page | 414 |
File Size | 194468 |
Page Count | 7 |
File Format | |
ISBN | 9780769528755 |
DOI | 10.1109/ICNC.2007.551 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-08-24 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Tree data structures Accuracy Technology management Linear regression Genetic programming Predictive models Technology forecasting Artificial intelligence Least squares methods Logistics |
Content Type | Text |
Resource Type | Article |
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