A Modified Genetic Programming for Behavior Scoring Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{chen:2007:CIDM,
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author = "Qing-Shan Chen and De-Fu Zhang and Li-Jun Wei and
Huo-Wang Chen",
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title = "A Modified Genetic Programming for Behavior Scoring
Problem",
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booktitle = "IEEE Symposium on Computational Intelligence and Data
Mining, CIDM 2007",
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year = "2007",
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pages = "535--539",
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address = "Honolulu, HI, USA",
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month = mar # " 1-" # apr # " 5",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Chinese
commercial bank, backpropagation neural network,
behavior scoring problem, financial institutions,
future credit performance forecasting, real life credit
data set, risk management, backpropagation, customer
relationship management, financial data processing",
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ISBN = "1-4244-0705-2",
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DOI = "doi:10.1109/CIDM.2007.368921",
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size = "5 pages",
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abstract = "Behavior scoring is an important part of risk
management in financial institutions, which is used to
help financial institutions make better decisions in
managing existing customers by forecasting their future
credit performance. In this paper, a modified genetic
programming (MGP) is introduced to solve the behavior
scoring problems. A real life credit data set in a
Chinese commercial bank is selected as the experimental
data to demonstrate the classification accuracy of this
method. MGP is compared with back-propagation neural
network (BPN), and another GP that uses normalized
inputs (NGP), the experimental results show that the
MGP has slight better classification accuracy rate than
NGP, and outperforms BPN in dealing with behavior
scoring problems because of less historical samples of
credit data in Chinese commercial banks",
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bibdate = "2007-09-13",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cidm/cidm2007.html#Qing-ShanDLH07",
- }
Genetic Programming entries for
Qing-Shan Chen
De-Fu Zhang
Li-Jun Wei
Huo-Wang Chen
Citations