A Modified Genetic Programming for Behavior Scoring Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{chen:2007:CIDM,

• author = "Qing-Shan Chen and De-Fu Zhang and Li-Jun Wei and Huo-Wang Chen",
• title = "A Modified Genetic Programming for Behavior Scoring Problem",
• booktitle = "IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007",
• year = "2007",
• pages = "535--539",
• address = "Honolulu, HI, USA",
• month = mar # " 1-" # apr # " 5",
• publisher = "IEEE",
• 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",
• ISBN = "1-4244-0705-2",
• DOI = "doi:10.1109/CIDM.2007.368921",
• size = "5 pages",
• 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",
• bibdate = "2007-09-13",
• 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