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Classification and Dimension Reduction in Bank Credit Scoring System

  • Bohan Liu
  • Bo Yuan
  • Wenhuang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5263)

Abstract

Customer credit is an important concept in the banking industry, which reflects a customer’s non-monetary value. Using credit scoring methods, customers can be assigned to different credit levels. Many classification tools, such as Support Vector Machines (SVMs), Decision Trees, Genetic Algorithms can deal with high-dimensional data. However, from the point of view of a customer manager, the classification results from the above tools are often too complex and difficult to comprehend. As a result, it is necessary to perform dimension reduction on the original customer data. In this paper, a SVM model is employed as the classifier and a “Clustering + LDA” method is proposed to perform dimension reduction. Comparison with some widely used techniques is also made, which shows that our method works reasonably well.

Keywords

Dimension Reduction LDA SVM Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bohan Liu
    • 1
  • Bo Yuan
    • 1
  • Wenhuang Liu
    • 1
  1. 1.Graduate School at ShenzhenTsinghua UniversityShenzhenP.R. China

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