Hybrid genetic algorithms and support vector machines for bankruptcy prediction

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Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, the support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as the neural network (NN) and logistic regression, and has shown good results. The genetic algorithm (GA) has been increasingly applied in conjunction with other Al techniques such as NN and Case-based reasoning (CBR). However, few studies have dealt with the integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both a feature subset and parameters of SVM simultaneously for bankruptcy prediction. (c) 2005 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2006-10
Language
English
Article Type
Article
Keywords

DISCRIMINANT-ANALYSIS; FINANCIAL RATIOS; NEURAL-NETWORKS; FAILURES

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.31, no.3, pp.652 - 660

ISSN
0957-4174
DOI
10.1016/j.eswa.2005.09.070
URI
http://hdl.handle.net/10203/3668
Appears in Collection
MT-Journal Papers(저널논문)
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