Variable Selection Method Affects SVM-based Models in Bankruptcy Prediction
Chih-Hung Wu 0, Wen-Chang Fang, Yeong-Jia Goo
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.114How to use a DOI?
- Variable Selection, Bankruptcy Prediction, Support vector machine.
- 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.
- 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 -