Journal of Systems Science and Complexity

, Volume 21, Issue 4, pp 527–539 | Cite as

Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

  • Lean YU
  • Shouyang WANG
  • Fenghua WEN
  • Kin Keung LAI
  • Shaoyi HE
Article

Abstract

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the dificulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.

Key words

Credit risk evaluation hybrid intelligent system rough sets support vector machine 

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Copyright information

© Academy of Mathematics & Systems Science, Beijing, China 2008

Authors and Affiliations

  • Lean YU
    • 1
  • Shouyang WANG
    • 1
  • Fenghua WEN
    • 2
  • Kin Keung LAI
    • 3
  • Shaoyi HE
    • 4
  1. 1.Institute of Systems Science, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.School of Economy and ManagementChangsha University of Science & TechnologyChangshaChina
  3. 3.Department of Management SciencesCity University of Hong KongKowloonHong Kong
  4. 4.Department of Information Systems and Operations Management, College of Business AdministrationCalifornia State University San MarcosSan MarcosUSA

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