Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function
Abstract
:1. Introduction
2. SVM and Fuzzy SVM
2.1. Standard Support Vector Machines
2.2. Fuzzy Support Vector Machines
3. Fuzzy SVM with SVDD Membership Function
4. Experimental Results and Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Original Attributes | Input Variables | Variable Type | Attribute Description |
---|---|---|---|
A1 | V1 | qualitative | Status of existing checking account |
A2 | V2 | numerical | Duration in month |
A3 | V3 | qualitative | Credit history |
A4 | V4,V5 | dummy | Purpose (V4: new car, V5: used car) |
A5 | V6 | numerical | Credit amount |
A6 | V7 | qualitative | Savings account/bonds |
A7 | V8 | qualitative | Present employment since |
A8 | V9 | qualitative | Personal status and sex |
A9 | V10,V11 | dummy | Other debtors/guarantors (V10: none, V11: co-applicant) |
A10 | V12 | numerical | Present residence since |
A11 | V13 | qualitative | Property |
A12 | V14 | numerical | Age in years |
A13 | V15 | qualitative | Other installment plans |
A14 | V16,V17 | dummy | Housing (V16: rent, V17: own) |
A15 | V18 | numerical | Number of existing credits at this bank |
A16 | V19,V20,V21 | dummy | Job (V19: unemployed/unskilled (non-resident), V20: unskilled (resident), V21: skilled employee/official) |
A17 | V22 | numerical | Number of people being liable to provide maintenance for |
A18 | V23 | qualitative | Telephone |
A19 | V24 | qualitative | foreign worker |
Methods | Sensitivity (%) | Specificity (%) | Accuracy (%) |
---|---|---|---|
SVDD-FSVM | 87.53 | 86.84 | 87.25 |
Nonlinear FSVM | 89.87 | 85.13 | 87.10 |
Linear FSVM | 86.95 | 86.48 | 86.67 |
Methods | Sensitivity (%) | Specificity (%) | Accuracy (%) |
---|---|---|---|
SVDD-FSVM | 89.59 | 48.60 | 77.30 |
Nonlinear FSVM | 92.15 | 41.75 | 77.00 |
Linear FSVM | 95.18 | 23.42 | 73.60 |
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Shi, J.; Xu, B. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function. J. Risk Financial Manag. 2016, 9, 13. https://doi.org/10.3390/jrfm9040013
Shi J, Xu B. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function. Journal of Risk and Financial Management. 2016; 9(4):13. https://doi.org/10.3390/jrfm9040013
Chicago/Turabian StyleShi, Jian, and Benlian Xu. 2016. "Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function" Journal of Risk and Financial Management 9, no. 4: 13. https://doi.org/10.3390/jrfm9040013