Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Variable Selection Method Affects SVM-based Models in Bankruptcy Prediction

Authors
Chih-Hung Wu 0, Wen-Chang Fang, Yeong-Jia Goo
Corresponding Author
Chih-Hung Wu
0Takming College
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.114How to use a DOI?
Keywords
Variable Selection, Bankruptcy Prediction, Support vector machine.
Abstract
This paper examined bankruptcy predictive accuracy of five statistics models--discriminant analysis logistic regression, probit regression, neural networks, support vector machine (SVM), and genetic-based SVM (GA-SVM) that influenced by variable selection. Empirical results indicate that the SVM-based models are very promising models for predicting financial failure, in terms of both best predictive accuracy and generalization ability. In addition, variable selection had the lowest influence of predictive accuracy in the GA-SVM model with optimal values of parameters.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.114How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Chih-Hung Wu
AU  - Wen-Chang Fang
AU  - Yeong-Jia Goo
PY  - 2006/10
DA  - 2006/10
TI  - Variable Selection Method Affects SVM-based Models in Bankruptcy Prediction
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.114
DO  - https://doi.org/10.2991/jcis.2006.114
ID  - Wu2006/10
ER  -