| Authors: |
Chorng-Shyong Ong
and Jih-Jeng Huang
and Gwo-Hshiung Tzeng
|
| URL: |
http://www.sciencedirect.com/science/article/B6V03-4F91Y8H-1/2/5116a9e3777103e0a563afc38e92e23a |
| Tags: |
(ANN),
Artificial
Credit
Decision
Rough
algorithms,
genetic
network
neural
programming,
scoring,
sets
trees,
|
| 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 to significantly improving the
accuracy of the credit scoring mode. genetic
programming (GP) is used to build credit scoring
models. Two numerical examples will be employed here to
compare the error rate to other credit scoring models
including the ANN, decision trees, rough sets, and
logistic regression. On the basis of the results, we
can conclude that GP can provide better performance
than other models. |
@article{Ong:2005:ESA,
title = {Building credit scoring models using genetic
programming},
author = {Chorng-Shyong Ong and Jih-Jeng Huang and Gwo-Hshiung Tzeng},
journal = {Expert Systems with Applications},
month = {July},
number = {1},
pages = {41--47},
url = {http://www.sciencedirect.com/science/article/B6V03-4F91Y8H-1/2/5116a9e3777103e0a563afc38e92e23a},
volume = {29},
year = {2005},
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 to significantly improving the
accuracy of the credit scoring mode. genetic
programming (GP) is used to build credit scoring
models. Two numerical examples will be employed here to
compare the error rate to other credit scoring models
including the ANN, decision trees, rough sets, and
logistic regression. On the basis of the results, we
can conclude that GP can provide better performance
than other models.},
owner = {wlangdon}, doi = {doi:10.1016/j.eswa.2005.01.003},
keywords = {(ANN), Artificial Credit Decision Rough algorithms, genetic network neural programming, scoring, sets trees, }
}
::