Application of support vector machines on the basis of the first Hungarian bankruptcy model Cover Image
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Application of support vector machines on the basis of the first Hungarian bankruptcy model
Application of support vector machines on the basis of the first Hungarian bankruptcy model

Author(s): Miklós Virag, Tamás Nyitrai
Subject(s): National Economy, Methodology and research technology, Policy, planning, forecast and speculation, Economic development, Public Finances
Published by: Akadémiai Kiadó
Keywords: bankruptcy prediction; classification; data preparation; outliers; support vector machines (SVM); ROC curve analysis;

Summary/Abstract: In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

  • Issue Year: 35/2013
  • Issue No: 2
  • Page Range: 227-248
  • Page Count: 22
  • Language: English