Abstract
There is a common agreement in many recent publications related to pattern recognition that dependency among classifier outputs plays a key role in combining classifiers [1]–[7]. Diversity, independence, disagreement and most recently negative dependency are the terms often used to express a desirable relation among classifiers to ensure the maximum improvement of the fusion system [4]–[7]. In this variety of concepts the idea is the same: how to measure relationship among classifiers from their outputs so that it is possible to say something about the combined classifier performance? Recent investigations indicate that error coincidences seem to be the most valuable information in this pursuit [7]–[9].
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
A.J.C. Sharkey, Combining Artificial Neural Nets: Ensemble and Modular Multi-net Systems, (Springer-Verlag, Berlin Heidelberg New York, 1999 ).
A.J.C. Sharkey, N.E. Sharkey, Combining Diverse Neural Nets. The Knowledge Engineering ReviewVol. 12 No. 3 (1997) pp. 231–247.
G. Rogova, Combining the results of several neural network classifiers. Neural NetworksVol. 5 No. 5 (1994) pp. 777–781.
D. Partridge, N. Griffith. Strategies for improving neural net generalization. Neural Computing & ApplicationsVol. 3 (1995) pp. 27–37.
L.I. Kuncheva, C.J. Whitaker, Ten Measures of Diversity in Classifier Ensembles: Limits for Two Classifiers. IEE Workshop on Intelligent Sensor Processing, Birmingham (2001), 10/1–10/6.
C.A. Shipp, L.I. Kuncheva, Relationships between combination methods and measures of diversity in combining classifiers. Information Fusion, accepted.
D. Ruta, B. Gabrys, Analysis of the Correlation Between Majority Voting Errors and the Diversity Measures in Multiple Classifier Systems. In Proceedings of the SOCO/ISFI2001 Conference, ISBN: 3–906454–27–4, Abstract page 50, Paper No. #1824–025, Paisley, UK, 2001.
D. Petridge, W.J. Krzanowski, Software diversity: practical statistics for its measurements and exploitation. Information & Software TechnologyVol. 39 (1997) pp. 707–717.
B. Littlewood, D.R. Miller, Conceptual modeling of coincident failures in multiversion software. IEEE Transactions on Software Engineering Vol. 15 No. 12 (1989) pp. 1596–1614.
H.-J. Kang, K. Kim, J.H. Kim, Optimal approximation of discrete probability distribution with kth-order dependency and its application to combining multiple classifiers. Pattern Recognition Letters Vol. 18 (1997) pp. 515–523.
K. Devlin, Joy of sets: fundamentals of contemporary set theory, 2nd eds., (Springer-Verlag New York 1993 ).
F. Ruskey, A Survey of Venn Diagrams. The Electronic Journal of Combinatorics eds. March 2001, (the electronic version available at: http://www.combinatorics.org/Surveys/ds5/VennEJC.html).
G. Giacinto, F. Roli, An approach to the automatic design of multiple classifier systems. Pattern Recognition LettersVol. 22 (2001) pp. 25–33.
L. Lam, C.Y. Suen, Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems, Man, and Cybernetics Vo. 27 No. 5 (1997) pp. 553–568.
D. Ruta, B. Gabrys. A theoretical analysis of the limits of majority voting errors for multiple classifier systems. To appear in Pattern Analysis and Applications.
R. Battiti, A.M. Colla, Democracy in neural nets: voting schemes for classification. Neural NetworksVol. 7 No. 4 (1994) pp. 691–707.
L.I. Kuncheva, C.J. Whitaker, C.A. Shipp, R.P.W Duin, Limits on the majority vote accuracy in classifier fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, accepted.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Kluwer Academic Publishers
About this chapter
Cite this chapter
Ruta, D., Gabrys, B. (2003). Set Analysis of Coincident Errors and Its Applications for Combining Classifiers. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_25
Download citation
DOI: https://doi.org/10.1007/978-1-4613-0231-5_25
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7952-2
Online ISBN: 978-1-4613-0231-5
eBook Packages: Springer Book Archive