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Credit Risk Evaluation Using SVM-Based Classifier

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Business Information Systems Workshops (BIS 2010)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 57))

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Abstract

This article presents a method joining Support Vector Machines (SVM), genetic search and multivariate analysis for identification of bankrupt companies. This study proposed to join widely used Altman Z-Score with Support Vector Machines to create a classifier that might be used to evaluate and forecast possible bankrupt companies. A genetic search algorithm is employed for relevant attribute selection to reduce the dimensionality of data.

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Author information

Authors and Affiliations

  1. Department of Informatics, Kaunas Faculty of Humanities, Vilnius University, Muitines St. 8, LT- 44280, Kaunas, Lithuania

    Paulius Danenas & Gintautas Garsva

Authors
  1. Paulius Danenas
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  2. Gintautas Garsva
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Editor information

Editors and Affiliations

  1. Department of Information Systems, Poznań University of Economics, Al. Niepodległości 10, 61-875, Poznań, Poland

    Witold Abramowicz

  2. Institut für Informatik, Freie Universität Berlin, Takustr. 9, 14195, Berlin, Germany

    Robert Tolksdorf

  3. Poznań University of Economics, Al. Niepodległości 10, 60-967, Poznań, Poland

    Krzysztof Węcel

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Danenas, P., Garsva, G. (2010). Credit Risk Evaluation Using SVM-Based Classifier. In: Abramowicz, W., Tolksdorf, R., Węcel, K. (eds) Business Information Systems Workshops. BIS 2010. Lecture Notes in Business Information Processing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15402-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-15402-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15401-0

  • Online ISBN: 978-3-642-15402-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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