Abstract
In this paper, we continue the theoretical and experimental analysis of two widely used combining rules, namely, the simple and weighted average of classifier outputs, that we started in previous works. We analyse and compare the conditions which affect the performance improvement achievable by weighted average over simple average, and over individual classifiers, under the assumption of unbiased and uncorrelated estimation errors. Although our theoretical results have been obtained under strict assumptions, the reported experiments show that they can be useful in real applications, for designing multiple classifier systems based on linear combiners.
This is a preview of subscription content, log in via an institution to check access.
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
Fumera, G., Roli, F.: Performance analysis and comparison of linear combiners for classifier fusion. In: Caelli, T., Amin, A., W. Duin, R.P., Kamel, M., de Ridder, D. (eds.): Proc. Joint Int. Workshops on Structural, Syntactic, and Statistical Pattern Recognition. LNCS, Vol. 2396. Springer-Verlag (2002) 424–432
Fumera, G., Roli, F., Vernazza, G.: Analysis of error-reject trade-off in linearly combined classifiers. In: Kasturi, R., Laurendeau, D., Suen, C. (eds.): Proc. 16th Int. Conf. on Pattern Recognition. Vol. 2 (2002) 120–123
Kittler, J., Alkoot, F. M.: Sum versus Vote Fusion in Multiple Classifier Systems. IEEE Trans. on Pattern Analysis and Machine Intelligence 25 (2003) 110–115
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20 (1998) 226–239
Kuncheva, L. I.: A theoretical study on six classifier fusion strategies. IEEE Trans. on Pattern Analysis and Machine Intelligence 24 (2002) 281–286
Lam, L., Suen, C.Y.: Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans. on Systems, Man and Cybernetics-Part A 27 (1997) 553–568
Perrone, M.P., Cooper, L.N.: When Networks Disagree: Ensemble Methods for Hybrid Neural Networks. In: Mammone, R.J. (ed.): Neural Networks for Speech and Image Processing. Chapman-Hall, New York (1993)
Roli, F.: Multisensor image recognition by neural networks with understandable behaviour. Int. Journal of Pattern Recognition and Artificial Intelligence 10 (1996) 887–917
Roli, F., Fumera, G.: Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers. In: Kittler, J., Roli, F. (eds.): Proc. of the 3rd Int. Workshop on Multiple Classifier Systems. LNCS, Vol. 2364. Springer-Verlag (2002) 252–261
Tumer, K., Ghosh, J.: Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition 29 (1996) 341–348
Tumer, K., Ghosh, J.: Linear and order statistics combiners for pattern classification. In: Sharkey, A.J.C. (ed.): Combining Artificial Neural Nets. Springer, London (1999) 127–155
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
Fumera, G., Roli, F. (2003). Linear Combiners for Classifier Fusion: Some Theoretical and Experimental Results. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_8
Download citation
DOI: https://doi.org/10.1007/3-540-44938-8_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40369-2
Online ISBN: 978-3-540-44938-6
eBook Packages: Springer Book Archive