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Credit Rating Using a Hybrid Voting Ensemble

  • Elias Kamos
  • Foteini Matthaiou
  • Sotiris Kotsiantis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7297)

Abstract

Credit risk analysis is an essential topic in the financial risk management. Credit risk analysis has been the main focus of financial and banking industry. A number of experiments have been conducted using representative supervised learning algorithms, which were trained using two public available credit datasets. The decision of which specific method to choose is a complex problem. Another option instead of choosing only one method is to create a hybrid ensemble of classifiers.

Keywords

Support Vector Machine Credit Risk Credit Rate Relevance Vector Machine Fuzzy Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Elias Kamos
    • 1
  • Foteini Matthaiou
    • 1
  • Sotiris Kotsiantis
    • 2
  1. 1.Hellenic Open UniversityGreece
  2. 2.Department of MathematicsUniversity of PatrasGreece

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