Two-stage genetic programming (2SGP) for the credit scoring model
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Huang:Tgp:06,
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author = "Jih-Jeng Huang and Gwo-Hshiung Tzeng and
Chorng-Shyong Ong",
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title = "Two-stage genetic programming {(2SGP)} for the credit
scoring model",
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journal = "Applied Mathematics and Computation",
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year = "2006",
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volume = "174",
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number = "2",
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pages = "1039--1053",
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month = "15 " # mar,
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keywords = "genetic algorithms, genetic programming, Credit
scoring model, Artificial neural network (ANN),
Decision trees, Rough sets, Two-stage genetic
programming (2SGP)",
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URL = "http://www.scorto.ru/downloads/Two-stage%20genetic%20programming%20(2SGP)%20for%20the%20credit%20scoring%20model.pdf",
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DOI = "doi:10.1016/j.amc.2005.05.027",
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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.",
- }
Genetic Programming entries for
Jih-Jeng Huang
Gwo-Hshiung Tzeng
Chorng-Shyong Ong
Citations