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.
Chapter PDF
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
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.
Bishop C.M. Neural Networks for Pattern Recognition. Oxford University Press, Walton Street, Oxford OX2 6DP, 1995.
Barnett V. and Lewis T. Outliers in statistical data. Wiley series in probability and mathematical statistics. John Wiley & Sons Ltd., 2nd edition, 1978.
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.
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.
Ripley B.D. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tax, D.M.J., Duin, R.P.W. (1998). Outlier detection using classifier instability. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033283
Download citation
DOI: https://doi.org/10.1007/BFb0033283
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64858-1
Online ISBN: 978-3-540-68526-5
eBook Packages: Springer Book Archive
