SVMT-Rule: Association Rule Mining Over SVM Classification Trees

  • Shaoning Pang
  • Nik Kasabov
Part of the Studies in Computational Intelligence book series (SCI, volume 80)

Since support vector machines (SVM) [7–9] demonstrate a good accuracy in classification and regression, rule extraction from a trained SVM (SVM-Rule) procedure is important for data mining and knowledge discovery [1–6, 29, 31]. However, the obtained rules from SVM-Rule in practice are less comprehensible than our expectation because there is a big number of incomprehensible numerical parameters (i.e., support vectors) turned up in those rules. Compared to SVM-Rule, decision-tree is a simple, but very efficient rule extraction method in terms of comprehensibility [33]. The obtained rules from decision tree may not be so accurate as SVM rules, but they are easy to comprehend because that every rule represents one decision path that is traceable in the decision tree.


Support Vector Machine Support Vector Terminal Node Association Rule Mining Rule Extraction 
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

  • Shaoning Pang
    • 1
  • Nik Kasabov
    • 1
  1. 1.Knowledge Engineering & Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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