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Support Vector Bankruptcy Prediction Model with Optimal Choice of RBF Kernel Parameter Values using Grid Search  

Min Jae H. (서강대학교 경영대학 경영학과)
Lee Young-Chan (동국대학교 상경대학 전자상거래학과)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.30, no.1, 2005 , pp. 55-74 More about this Journal
Abstract
Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.
Keywords
Bankruptcy Prediction; Support Vector Machine; Grid Search; Kernel Function; Back-Propagation Neural Networks;
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