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SVMT-Rule: Association Rule Mining Over SVM Classification Trees

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Rule Extraction from Support Vector Machines

Part of the book series: Studies in Computational Intelligence ((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.

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Authors and Affiliations

  1. Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, 1020, New Zealand

    Shaoning Pang & Nik Kasabov

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  1. Shaoning Pang
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  2. Nik Kasabov
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Editors and Affiliations

  1. School of Information Technology and Electrical Engineering School of Medicine, Central Clinical Division, The University of Queensland, Brisbane, Q 4072, Australia

    Joachim Diederich (Honorary Professor) (Honorary Professor)

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Pang, S., Kasabov, N. (2008). SVMT-Rule: Association Rule Mining Over SVM Classification Trees. In: Diederich, J. (eds) Rule Extraction from Support Vector Machines. Studies in Computational Intelligence, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75390-2_6

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  • DOI: https://doi.org/10.1007/978-3-540-75390-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75389-6

  • Online ISBN: 978-3-540-75390-2

  • eBook Packages: EngineeringEngineering (R0)

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