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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Applied mathematics and computation Ročník 174; číslo 2; s. 1039 - 1053
Hlavní autoři: Huang, Jih-Jeng, Tzeng, Gwo-Hshiung, Ong, Chorng-Shyong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY Elsevier Inc 15.03.2006
Elsevier
Témata:
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)
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
ISSN:0096-3003, 1873-5649
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.05.027