International Journal of Computational Intelligence Systems

Volume 3, Issue 6, December 2010, Pages 797 - 804

A Modified Support Vector Machine model for Credit Scoring

Authors
Xiaoyong Liu, Hui Fu, Weiwei Lin
Corresponding Author
Xiaoyong Liu
Received 11 March 2010, Accepted 5 October 2010, Available Online 1 December 2010.
DOI
10.2991/ijcis.2010.3.6.10How to use a DOI?
Keywords
credit scoring, Support vector machine, Genetic algorithm, Radial Basis Kernel
Abstract

This paper presents a novel quantitative credit scoring model based on support vector machine (SVM) with adaptive genetic algorithm, gr-GA-SVM. In this study, two real world credit datasets in the University of California Irvine Machine Learning Repository are selected for the numerical experiments. SVM, GA-SVM and gr-GA-SVM, are employed to predict the accuracy of credit scoring in two datasets. Numerical results indicate that gr-GA-SVM is more accurate and efficient than SVM and GA-SVM.

Copyright
© 2010, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
3 - 6
Pages
797 - 804
Publication Date
2010/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2010.3.6.10How to use a DOI?
Copyright
© 2010, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Xiaoyong Liu
AU  - Hui Fu
AU  - Weiwei Lin
PY  - 2010
DA  - 2010/12/01
TI  - A Modified Support Vector Machine model for Credit Scoring
JO  - International Journal of Computational Intelligence Systems
SP  - 797
EP  - 804
VL  - 3
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.6.10
DO  - 10.2991/ijcis.2010.3.6.10
ID  - Liu2010
ER  -