Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring

  • David Martens
  • Johan Huysmans
  • Rudy Setiono
  • Jan Vanthienen
  • Bart Baesens
Part of the Studies in Computational Intelligence book series (SCI, volume 80)


Innovative storage technology and the rising popularity of the Internet have generated an ever-growing amount of data. In this vast amount of data much valuable knowledge is available, yet it is hidden. The Support Vector Machine (SVM) is a state-of-the-art classification technique that generally provides accurate models, as it is able to capture non-linearities in the data. However, this strength is also its main weakness, as the generated non-linear models are typically regarded as incomprehensible black-box models. By extracting rules that mimic the black box as closely as possible, we can provide some insight into the logics of the SVM model. This explanation capability is of crucial importance in any domain where the model needs to be validated before being implemented, such as in credit scoring (loan default prediction) and medical diagnosis. If the SVM is regarded as the current state-of-the-art, SVM rule extraction can be the state-of-the-art of the (near) future. This chapter provides an overview of recently proposed SVM rule extraction techniques, complemented with the pedagogical Artificial Neural Network (ANN) rule extraction techniques which are also suitable for SVMs. Issues related to this topic are the different rule outputs and corresponding rule expressiveness; the focus on high dimensional data as SVM models typically perform well on such data; and the requirement that the extracted rules are in line with existing domain knowledge. These issues are explained and further illustrated with a credit scoring case, where we extract a Trepan tree and a RIPPER rule set from the generated SVM model. The benefit of decision tables in a rule extraction context is also demonstrated. Finally, some interesting alternatives for SVM rule extraction are listed.


Support Vector Machine Class Label Support Vector Machine Model Decision Boundary Decision Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David Martens
    • 1
  • Johan Huysmans
    • 1
  • Rudy Setiono
    • 2
  • Jan Vanthienen
    • 1
  • Bart Baesens
    • 1
    • 3
  1. 1.Department of Decision Sciences and Information ManagementK.U.LeuvenLeuvenBelgium
  2. 2.School of ComputingNational University of SingaporeSingaporeSingapore
  3. 3.School of ManagementUniversity of SouthamptonHighfield SouthamptonUK

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