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An application of support vector machines in bankruptcy prediction model

Title
An application of support vector machines in bankruptcy prediction model
Authors
Shin K.-S.Lee T.S.Kim H.-J.
Ewha Authors
신경식김현정
SCOPUS Author ID
신경식scopus; 김현정scopus
Issue Date
2005
Journal Title
Expert Systems with Applications
ISSN
0957-4174JCR Link
Citation
vol. 28, no. 1, pp. 127 - 135
Indexed
SCIE; SCOPUS WOS scopus
Abstract
This study investigates the efficacy of applying support vector machines (SVM) to bankruptcy prediction problem. Although it is a well-known fact that the back-propagation neural network (BPN) performs well in pattern recognition tasks, the method has some limitations in that it is an art to find an appropriate model structure and optimal solution. Furthermore, loading as many of the training set as possible into the network is needed to search the weights of the network. On the other hand, since SVM captures geometric characteristics of feature space without deriving weights of networks from the training data, it is capable of extracting the optimal solution with the small training set size. In this study, we show that the proposed classifier of SVM approach outperforms BPN to the problem of corporate bankruptcy prediction. The results demonstrate that the accuracy and generalization performance of SVM is better than that of BPN as the training set size gets smaller. We also examine the effect of the variability in performance with respect to various values of parameters in SVM. In addition, we investigate and summarize the several superior points of the SVM algorithm compared with BPN. © 2004 Elsevier Ltd. All rights reserved.
DOI
10.1016/j.eswa.2004.08.009
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
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