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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)

Abstract

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.

Keywords

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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References

  1. 1.
    J.A. Benediktsson and P.H. Swain. Consensus theoretic classification methods. IEEE Transactions on Systems, Man and Cybernetics, 22(4):688–704, July/August 1992.zbMATHCrossRefGoogle Scholar
  2. 2.
    A.P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145–1159, 1997.CrossRefGoogle Scholar
  3. 3.
    G.A. Carpenter, S. Grossberg, and D.B. Rosen. ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition. Neural Networks, 4(4):493–504, 1991.CrossRefGoogle Scholar
  4. 4.
    R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, New York, 1973.zbMATHGoogle Scholar
  5. 5.
    R.P.W. Duin. UCI dataset, multiple features database. Available from ftp://ftp.ics.uci.edu/pub/machine-learning-databases/mfeat/, 1999.
  6. 6.
    N. Japkowicz. Concept-Learning in the absence of counter-examples: an autoassociation-based approach to classification. PhD thesis, New Brunswick Rutgers, The State University of New Jersey, 1999.Google Scholar
  7. 7.
    J. Kittler, R.P.W. Duin, and J. Matas. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(4):226–239, 1998.CrossRefGoogle Scholar
  8. 8.
    J. Kittler, A. Hojjatoleslami, and T. Windeatt. Weighting factors in multiple expert fusion. In Clark A.F., editor, Proceedings of the 8th British Machine Vision Conference 1997, pages 41–50. University of Essex Printing Service, 1997.Google Scholar
  9. 9.
    M.A. Kraaijveld and R.P.W. Duin. A criterion for the smoothing parameter for parzen-estimators of probability density functions. Technical report, Delft University of Technology, September 1991.Google Scholar
  10. 10.
    M.R. Moya, M.W. Koch, and L.D. Hostetler. 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
  11. 11.
    M. Tanigushi and V. Tresp. Averaging regularized estimators. Neural Computation, 9:1163–1178, 1997.CrossRefGoogle Scholar
  12. 12.
    L. Tarassenko, P. Hayton, and M. Brady. Novelty detection for the identification of masses in mammograms. In Proc. of the Fourth International IEE Conference on Artificial Neural Networks, volume 409, pages 442–447, 1995.CrossRefGoogle Scholar
  13. 13.
    D.M.J. Tax and R.P.W Duin. Data domain description using support vectors. In M. Verleysen, editor, Proceedings of the European Symposium on Artificial Neural Networks 1999, pages 251–256. D.Facto, Brussel, April 1999.Google Scholar
  14. 14.
    D.M.J. Tax and R.P.W Duin. Support vector domain description. Pattern Recognition Letters, 20(11-13):1191–1199, December 1999.CrossRefGoogle Scholar
  15. 15.
    A. Ypma and R.P.W. Duin. Support objects for domain approximation. In ICANN’98, Skovde (Sweden), September 1998.Google Scholar

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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