A Fuzzy Integral Method of Applying Support Vector Machine for Multi-class Problem

  • Yanning Zhang
  • Hejin Yuan
  • Jin Pan
  • Ying Li
  • Runping Xi
  • Lan Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


This paper proposed a novel method of applying support vector machine for multi-class problem based on fuzzy integral. Firstly, the fuzzy measure of each binary classifier is constructed based on its classification accuracy during training and its agreement degrees to other support vector machines. Then the testing instances are classified by calculating the fuzzy integral between the fuzzy measures and the outputs of the binary support vector machines. The experiment results on iris and glass datasets from UCI machine learning repository and real plane dataset show that the new method is effective. And the experiment results ulteriorly indicate that the method with Choquet fuzzy integral has better performance than that with Sugeno integral.


Support Vector Machine Binary Classifier Fuzzy Measure Multiclass Classification Binary Support Vector Machine 
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 2006

Authors and Affiliations

  • Yanning Zhang
    • 1
  • Hejin Yuan
    • 1
  • Jin Pan
    • 2
  • Ying Li
    • 1
  • Runping Xi
    • 1
  • Lan Yao
    • 1
  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Lab of Network Security and CountermeasureXi’an Communication InstituteXi’anChina

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