Combining One-Class Classifiers

  • David M. J. Tax
  • Robert P. W. Duin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)


In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is assumed that only information of the target class is available while nothing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the classifiers with poorer performance might waste valuable information. To improve performance the results of different classifiers (which may differ in complexity or training algorithm) can be combined. This can not only increase the performance but it can also increase the robustness of the classification. Because for one-class classifiers only information of one of the classes is present, combining one-class classifiers is more difficult. In this paper we investigate if and how one-class classifiers can be combined best in a handwritten digit recognition problem.


Target Object Target Class Combination Rule Weighted Vote Support Vector Data Description 
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 2001

Authors and Affiliations

  • David M. J. Tax
    • 1
  • Robert P. W. Duin
    • 1
  1. 1.Pattern Recognition GroupDelft University of TechnologyThe Netherlands

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