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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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.
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.
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.
R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, New York, 1973.
R.P.W. Duin. UCI dataset, multiple features database. Available from ftp://ftp.ics.uci.edu/pub/machine-learning-databases/mfeat/, 1999.
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.
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.
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.
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.
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.
M. Tanigushi and V. Tresp. Averaging regularized estimators. Neural Computation, 9:1163–1178, 1997.
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.
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.
D.M.J. Tax and R.P.W Duin. Support vector domain description. Pattern Recognition Letters, 20(11-13):1191–1199, December 1999.
A. Ypma and R.P.W. Duin. Support objects for domain approximation. In ICANN’98, Skovde (Sweden), September 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tax, D.M.J., Duin, R.P.W. (2001). Combining One-Class Classifiers. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_30
Download citation
DOI: https://doi.org/10.1007/3-540-48219-9_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42284-6
Online ISBN: 978-3-540-48219-2
eBook Packages: Springer Book Archive