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)

Abstract

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.

Keywords

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.

References

  1. [BDT97]
    Breukelen van M., Duin R.P.W, and Tax D.M.J. Combining classifiers for the recognition of handwritten digits. In Pudil P., Novovicova J, and Grim J, editors, 1st international workshop on statistical techniques in pattern recognition, pages 13–18. Institute of Information Theory and Automation, June 1997.Google Scholar
  2. [Bis95]
    Bishop C.M. Neural Networks for Pattern Recognition. Oxford University Press, Walton Street, Oxford OX2 6DP, 1995.Google Scholar
  3. [BL78]
    Barnett V. and Lewis T. Outliers in statistical data. Wiley series in probability and mathematical statistics. John Wiley & Sons Ltd., 2nd edition, 1978.Google Scholar
  4. [FH89]
    Fukunaga, K. and Hummels D.M. Leave-one-out procedures for nonparametric error estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(4):421–423, April 1989.CrossRefGoogle Scholar
  5. [MKH93]
    Moya, MR., Koch, M.W., and Hostetler, L.D. One-class classifier networks for target recognition applications. In Proceedings world congress on neural networks, pages 797–801, Portland, OR, 1993. International Neural Network Society, INNS.Google Scholar
  6. [Rip96]
    Ripley B.D. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.Google Scholar

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

Personalised recommendations