Variable Selection Method Affects SVM Approach in Bankruptcy Prediction

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.