Variable Selection Method Affects SVM-based Models in Bankruptcy Prediction

DOI

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.

Copyright

© 2006, 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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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  - Proceedings of the 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  - 10.2991/jcis.2006.114
ID  - Wu2006/10
ER  -