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Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005)
  • Young-Chan Lee, Prof., Dongguk University
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 applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, we compare its performance with those of 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 Machines,
  • Grid Search,
  • Kernel Function,
  • Back-propagation Neural Networks
Publication Date
Winter December, 2005
Citation Information
Young-Chan Lee. "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters" Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Vol. 28 Iss. 4 (2005)
Available at: http://works.bepress.com/yclee/3/