A Novel Hybrid Data Mining Framework for Credit Evaluation

  • Yatao Yang
  • Zibin Zheng
  • Chunzhen Huang
  • Kunmin Li
  • Hong-Ning DaiEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


Internet loan business has received extensive attentions recently. How to provide lenders with accurate credit scoring profiles of borrowers becomes a challenge due to the tremendous amount of loan requests and the limited information of borrowers. However, existing approaches are not suitable to Internet loan business due to the unique features of individual credit data. In this paper, we propose a unified data mining framework consisting of feature transformation, feature selection and hybrid model to solve the above challenges. Extensive experiment results on realistic datasets show that our proposed framework is an effective solution.


Credit evaluation Data mining Internet finance 



The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China under (61472338), the Fundamental Research Funds for the Central Universities, and Macao Science and Technology Development Fund under Grant No. 096/2013/A3.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Yatao Yang
    • 1
  • Zibin Zheng
    • 1
    • 2
  • Chunzhen Huang
    • 1
  • Kunmin Li
    • 1
  • Hong-Ning Dai
    • 3
    Email author
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Collaborative Innovation Center of High Performance ComputingNational University of Defense TechnologyChangshaChina
  3. 3.Faculty of Information TechnologyMacau University of Science and TechnologyTaipaMacau SAR

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