Abstract
This paper proposes the use of information about the distribution of the classifier outputs in their output spaces during combination. Two different methods based on the clustering in the output spaces are developed. In the first approach, taking into account the distribution of the output vectors in these clusters, the local reliability of each individual classifier is quantified and used for weighting the classifier outputs during combination. In the second method, the classifier outputs are replaced by the centroids of the nearest clusters during combination. Experimental results have shown that both of the proposed approaches provide more than 3% improvement in the correct classification rate.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kittler, J., et al.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Altınçay, H., Demirekler, M.: An information theoretic framework for weight estimation in the combination of probabilistic classifiers for speaker identification. Speech Communication 30(4), 255–272 (2000)
Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision templates for multiple classifier fusion. Pattern Recognition 34(2), 299–314 (2001)
Denker, J.S., leCun, Y.: Transforming neural-net output levels to probability distributions. Technical report, AT&T Bell Laboratories (1991)
Duin, R.P.W., Tax, M.J.: Classifier conditional posteriori probabilities. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 611–619. Springer, Heidelberg (1998)
Giacinto, G., Roli, F.: Methods for dynamic classifier selection. In: ICIAP 1999, 10th international conference on image analysis and processing, Italy, September 1999, pp. 659–664 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Chichester (2000)
Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Transactions on Communications 28, 84–95 (1980)
Benediktsson, J.A., Swain, P.H.: Consensus theoretic classification methods. IEEE Transactions on Systems Man and Cybernetics 22(4), 688–704 (1992)
Gish, H., Schmidt, M.: Text-independent speaker identification. IEEE Signal Processing Magazine, 18–32 (October 1994)
Campbell, J.P.: Speaker recognition: A tutorial. Proceedings of the IEEE 85(9), 1437–1462 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Altınçay, H., Çizili, B. (2003). Classifier Combination through Clustering in the Output Spaces. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_60
Download citation
DOI: https://doi.org/10.1007/978-3-540-45179-2_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40730-0
Online ISBN: 978-3-540-45179-2
eBook Packages: Springer Book Archive