Abstract
At present, fixed rules for classifier combination are the most used and widely investigated ones, while the study and application of trained rules has received much less attention. Therefore, pros and cons of fixed and trained rules are only partially known even if one focuses on crisp classifier outputs. In this paper, we report the results of an experimental comparison of well-known fixed and trained rules for crisp classifier outputs. Reported experiments allow one draw some preliminary conclusions about comparative advantages of fixed and trained fusion rules.
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.
References
C.Y. Suen, L. Lam. Multiple classifier combination methodologies for different output levels, First International Workshop on Multiple Classifier Systems (MCS 2000). Springer-Verlag Pub., Lecture Notes in Computer Science, Vol. 1857, J. Kittler and F. Roli Eds., (2000) 52–66
Huang, Y.S. and Suen, C.Y. A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. On Pattern Analysis and Machine Intelligence 17(1) (1995) 90–94.
R.P.W. Duin, et al. A Discussion on Trained Combining of Classifiers, concept paper, January 2001, see: http://hades.ph.ta.tudelft.nl/~bob/papers/TrainedCombiners.pdf
L.I. Kuncheva. Combinations of multiple classifiers using fuzzy sets, in Fuzzy Classifier Design (Chapter 8), Springer Verlag, May 2000, pp. 233–267
S. Raudys. Combining the expert networks: a review. Proc. of the Int. Conference on Neural Networks and Artificial Intelligence, (R. Sadykhov Ed.), 2–5 October 2001, Minsk, Belorus, pp. 81–91
G. Giacinto, F. Roli, and L. Bruzzone. Combination of Neural and Statistical Algorithms for Supervised Classification of Remote-Sensing Images. Pattern Recognition Letters, May 2000, vol. 21, no. 5, pp. 385–397
M. Skurichina, S. Raudys, R.P.W. Duin. K-nearest neighbours directed noise injection in multilayer perceptron training. IEEE Trans, on Neural Networks, Vol. 11, 2000, pp. 504–511
S. Raudys. Experts’ bias in trainable fusion rules, submitted to IEEE Trans. on PAMI
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(5), 1997, 553–568
Kucheva, L.I., et al.. Is independence good for combining classifiers? Proc. of ICPR2000, 15th Int. Conference on Pattern Recognition, Barcelona, Spain, September 3–8, 2000, Vol. 2, pp. 168–171
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roli, F., Raudys, Š., Marcialis, G.L. (2002). An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs. In: Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2002. Lecture Notes in Computer Science, vol 2364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45428-4_23
Download citation
DOI: https://doi.org/10.1007/3-540-45428-4_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43818-2
Online ISBN: 978-3-540-45428-1
eBook Packages: Springer Book Archive