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Credit Scoring Using PCA-SVM Hybrid Model

  • M. A. H. Farquad
  • V. Ravi
  • Sriramjee
  • G. Praveen
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)

Abstract

A new approach for credit scoring using principal component analysis (PCA) and support vector machine (SVM) in tandem is proposed in this paper. The proposed credit scoring hybrid algorithm consists of two basic steps. In the first step, PCA is employed for dimension reduction and in the second, SVM is employed for classification purpose, resulting in PCA-SVM hybrid model. The effectiveness of PCA-SVM model is evaluated using German and UK credit data sets. It is observed that PCA-SVM outperforms stand-alone SVM and PCA-Logistic Regression (LR) hybrid. However, in terms of sensitivity alone, LR outperformed PCA-SVM hybrid.

Keywords

Credit Scoring SVM PCA Logistic Regression dimension reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. A. H. Farquad
    • 1
    • 2
  • V. Ravi
    • 1
  • Sriramjee
    • 3
  • G. Praveen
    • 4
  1. 1.Institute for Development and Research in Banking TechnologyHyderabadIndia
  2. 2.Department of Computer & Information SciencesUniversity of HyderabadHyderabadIndia
  3. 3.Department of MathematicsIndian Institute of TechnologyKharagpurIndia
  4. 4.Department of MathematicsIndian Institute of TechnologyNew DelhiIndia

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