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A Hybrid Support Vector Machine Ensemble Model for Credit Scoring
by
Ahmad Ghodselahi
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 17 - Number 5 |
| Year of Publication: 2011 |
| Authors: Ahmad Ghodselahi |
10.5120/2220-2829
|
Ahmad Ghodselahi . A Hybrid Support Vector Machine Ensemble Model for Credit Scoring. International Journal of Computer Applications. 17, 5 ( March 2011), 1-5. DOI=10.5120/2220-2829
@article{
10.5120/2220-2829,
author = {
Ahmad Ghodselahi
},
title = { A Hybrid Support Vector Machine Ensemble Model for Credit Scoring },
journal = {
International Journal of Computer Applications
},
issue_date = { March 2011 },
volume = { 17 },
number = { 5 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = {
1-5
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume17/number5/2220-2829/
},
doi = { 10.5120/2220-2829 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:46.630617+05:30
%A Ahmad Ghodselahi
%T A Hybrid Support Vector Machine Ensemble Model for Credit Scoring
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 5
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Credit risk is the most challenging risk to which financial institution are exposed. Credit scoring is the main analytical technique for credit risk assessment. In this paper a hybrid model for credit scoring is designed which applies ensemble learning for credit granting decisions. The hybrid model combines clustering and classification techniques. Ten Support Vector Machine (SVM) classifiers are utilized as the members of ensemble model. Since even a small improvement in credit scoring accuracy causes significant loss reduction, then the application of ensemble in hybrid model leads to better performance of classification. A real dataset is used to test the model performance. The test results shows that proposed hybrid SVM ensemble has better classification accuracy when compared with other methods.
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Index Terms
Computer Science
Information Sciences
Keywords