Outlier detection using classifier instability

  • David M. J. Tax
  • Robert P. W. Duin
Statistical Classification Techniques
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


When a classifier is used to classify objects, it is important to know if these objects resemble the training objects the classifier is trained with. Several methods to detect novel objects exist. In this paper a new method is presented which is based on the instability of the output of simple classifiers on new objects. The performances of the outlier detection methods is shown in a handwritten digit recognition problem.


Gaussian Mixture Model Outlier Detection Simple Classifier Training Object Probability Density Estimation 
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 1998

Authors and Affiliations

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

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