A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Zhang:2008:ICNC,
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author = "Defu Zhang and Mhand Hifi and Qingshan Chen and
Weiguo Ye",
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title = "A Hybrid Credit Scoring Model Based on Genetic
Programming and Support Vector Machines",
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booktitle = "Fourth International Conference on Natural
Computation, ICNC '08",
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year = "2008",
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month = oct,
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volume = "7",
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pages = "8--12",
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keywords = "genetic algorithms, genetic programming, UCI database,
back-propagation neural network, credit industry,
decision tree classifiers, hybrid credit scoring model,
logistic regression, support vector machines, financial
data processing, support vector machines",
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DOI = "doi:10.1109/ICNC.2008.205",
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abstract = "Credit scoring has obtained more and more attention as
the credit industry can benefit from reducing potential
risks. Hence, many different useful techniques, known
as the credit scoring models, have been developed by
the banks and researchers in order to solve the
problems involved during the evaluation process. In
this paper, a hybrid credit scoring model (HCSM) is
developed to deal with the credit scoring problem by
incorporating the advantages of genetic programming and
support vector machines. Two credit data sets in UCI
database are selected as the experimental data to
demonstrate the classification accuracy of the HCSM.
Compared with support vector machines, genetic
programming, decision tree classifiers, logistic
regression, and back-propagation neural network, HCSM
can obtain better classification accuracy.",
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notes = "Also known as \cite{4667935}",
- }
Genetic Programming entries for
De-Fu Zhang
Mhand Hifi
Qing-Shan Chen
Weiguo Ye
Citations