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/).
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 -