This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Theoretical Study on Six Classifier Fusion Strategies
February 2002 (vol. 24 no. 2)
pp. 281-286

Abstract—We look at a single point in the feature space, two classes, and $L$ classifiers estimating the posterior probability for class $\omega_1$. Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle.

[1] F.M. Alkoot and J. Kittler, “Experimental Evaluation of Expert Fusion Strategies,” Pattern Recognition Letters, vol. 20, no. 11, pp. 11-13, 1999.
[2] C.M. Bishop, Neural Networks for Pattern Recognition. Clarendon Press, 1995.
[3] L. Breiman, “Combining Predictors,” Combining Artificial Neural Nets, A. Sharkey, ed., pp. 31–50, 1999.
[4] J. Kittler, M. Hatef, R. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[5] L. Kuncheva, J. Bezdek, and R. Duin, “Decision Templates for Multiple Classifier Fusion: An Experimental Comparison,” Pattern Recognition, vol. 34, no. 2, pp. 299–314, 2001.
[6] L. Kuncheva and C. Whitaker, “Ten Measures of Diversity in Classifier Ensembles: Limits for Two Classifiers,” Proc. IEE Workshop Intelligent Sensor Processing, pp. 10/1–10/6, Feb. 2001.
[7] Bev Littlewood and Douglas R. Miller, "Conceptual Modeling of Coincident Failures in Multiversion Software," IEEE Transactions on Software Engineering, vol. 15, p. 1,596, Dec. 1989.
[8] A. Mood, F. Graybill, and D. Boes, Introduction to the Theory of Statistics, third ed. McGraw-Hill, 1974.
[9] R. Schapire, “Theoretical Views of Boosting,” Proc. Fourth European Conf. Computational Learning Theory, pp. 1–10, 1999.
[10] Combining Artificial Neural Nets. Ensemble and Modular Multi-Net Systems. A. Sharkey, ed., London: Springer-Verlag, 1999.
[11] D. Tax, R. Duin, and M. van Breukelen, “Comparison between Product and Mean Classifier Combination Rules,” Proc. Workshop Statistical Pattern Recognition, 1997.
[12] K. Tumer and J. Ghosh, “Error Correlation and Error Reduction in Ensemble Classifiers,” Connection Science, vol. 8, nos. 3 and 4, pp. 385–404, 1996.
[13] K. Tumer and J. Ghosh, “Linear and Order Statistics Combiners for Pattern Classification,” Combining Artificial Neural Nets, A. Sharkey, ed., pp. 127–161, 1999.
[14] M. van Breukelen, R. Duin, D. Tax, and J. den Hartog, “Combining Classifiers for the Recognition of Handwritten Digits,” Proc. First IAPR TC1 Workshop Statistical Techniques in Pattern Recognition, pp. 13–18, 1997.

Index Terms:
Classifier combination, theoretical error, fusion methods, order statistics, majority vote, independent classifiers.
Citation:
Ludmila I. Kuncheva, "A Theoretical Study on Six Classifier Fusion Strategies," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 281-286, Feb. 2002, doi:10.1109/34.982906
Usage of this product signifies your acceptance of the Terms of Use.