Two-stage genetic programming (2SGP) for the credit scoring model
Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an...
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| Vydáno v: | Applied mathematics and computation Ročník 174; číslo 2; s. 1039 - 1053 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
Elsevier Inc
15.03.2006
Elsevier |
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| ISSN: | 0096-3003, 1873-5649 |
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| Abstract | Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF–THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models. |
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| AbstractList | Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF–THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models. |
| Author | Huang, Jih-Jeng Ong, Chorng-Shyong Tzeng, Gwo-Hshiung |
| Author_xml | – sequence: 1 givenname: Jih-Jeng surname: Huang fullname: Huang, Jih-Jeng organization: Department of Information Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan – sequence: 2 givenname: Gwo-Hshiung surname: Tzeng fullname: Tzeng, Gwo-Hshiung email: u5460637@ms16.hinet.net organization: Institute of Management of Technology and Institute of Traffic and Transportation College of Management, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu 300, Taiwan – sequence: 3 givenname: Chorng-Shyong surname: Ong fullname: Ong, Chorng-Shyong organization: Department of Information Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan |
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| Keywords | Artificial neural network (ANN) Credit scoring model Decision trees Two-stage genetic programming (2SGP) Rough sets Decision tree Discriminant function Statistical method Accuracy Numerical analysis Applied mathematics Genetic programming Artificial intelligence |
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| SubjectTerms | Applied sciences Artificial intelligence Artificial neural network (ANN) Computer science; control theory; systems Credit scoring model Decision trees Exact sciences and technology Learning and adaptive systems Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming Numerical methods in mathematical programming, optimization and calculus of variations Rough sets Sciences and techniques of general use Two-stage genetic programming (2SGP) |
| Title | Two-stage genetic programming (2SGP) for the credit scoring model |
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