Soft Computing

, 13:149

Credit scoring using support vector machines with direct search for parameters selection

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DOI: 10.1007/s00500-008-0305-0

Cite this article as:
Zhou, L., Lai, K.K. & Yu, L. Soft Comput (2009) 13: 149. doi:10.1007/s00500-008-0305-0

Abstract

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.

Keywords

Credit scoringDirect searchSupport vector machinesGenetic algorithm

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of Management SciencesCity University of Hong KongKowloonHong Kong
  2. 2.Institute of Systems ScienceAcademy of Mathematics and Systems Science, Chinese Academy of SciencesBeijingChina