Abstract
At present, the usual operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers have pointed out the potentialities of “dynamic classifier selection’ as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper is aimed to provide a theoretical framework for dynamic classifier selection and to define the assumptions under which it can be expected to improve the accuracy of the individual classifiers. To this end, dynamic classifier selection is placed in the general framework of statistical decision theory and it is shown that, under some assumptions, the optimal Bayes classifier can be obtained by selecting non-optimal classifiers. Two classifier selection methods that derive from the proposed framework are described. The experimental results obtained in the classification of remote-sensing images and comparisons among different combination methods are reported.
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References
Xu, L., Krzyzak A., and Suen C.Y., Methods for combining multiple classifiers and their applications to handwriting recognition, IEEE Trans. on Systems, Man, and Cyb. 22 (1992) 418–435.
Lam, L. and 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.
Ho, T.K., Hull, J.J., and Srihari S.N., “Decision combination in multiple classifiers systems”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 16, No. 1, Jan. 1994, pp. 66–75.
Battiti, R.and Colla A.M., Democracy in neural Nets: Voting Schemes for Classification, Neural Networks 7 (1994) 691–707.
Kittler, J., Hatef M., Duin R.P.W. and Matas J., On Combining Classifiers, IEEE Trans. on Pattern Analysis and Machine Intelligence 20 (1998) 226–239.
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 (1995) 90–94.
Wolpert, D.H., Stacked generalisation, Neural Networks 5 (1992) 241–259.
Sharkey, A., Multi-Net Systems, Combining Artificial Neural Nets, Ensemble and Modular Multi-Net Systems, Springer-Verlag, 1999, pp. 1–27
Breiman, L., Bagging Predictors, Machine Learning, 24(2), 1996, pp. 123–140
Dietterich, T.G., An experimental comparison of three methods for constructing ensembles of decisions trees: Bagging, boosting, and randomization, Machine Learning, 2000, in press
Suen C.Y., and Lam L., Multiple classifier combination methodologies for different output levels, Proc. of the First International Workshop on Multiple Classifier Systems (MCS2000), June 21–23 2000, Cagliari, Italy, Springer-Verlag Ed., in press
Tumer K., and Ghosh J., Linear and order statistics combiners for pattern classification, Combining Artificial Neural Nets, Ensemble and Modular Multi-Net Systems, Springer-Verlag, 1999, pp. 127–157
Hansen, L. K. and Salamon P., Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 993–1001.
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, in press
Giacinto G., and Roli F., Design of effective neural network ensembles for image classification purposes, Image and Vision Computing Journal, 2000, in press
Sharkey A., Sharkey N.E., Gerecke U., Chandroth G.O., The “test and select” approach to ensemble combination, Proc. of the First International Workshop on Multiple Classifier Systems (MCS2000), June 21–23 2000, Cagliari, Italy, Springer-Verlag Ed., in press
Partridge, D. and Yates W.B., Engineering multiversion neural-net systems, Neural Computation 8 (1996) 869–893.
Opitz, D.W. and Shavlik J.W., Actively searching for an effective neural network ensemble, Connection Science 8 (1996) 337–353.
Giacinto, G., and Roli, F., “Adaptive Selection of Image Classifiers”. Proc. of the 9th Int. Conference on Image Analysis and Processing, Lecture Notes in Computer Science 1310, Springer Verlag Ed., 1997, pp. 38–45.
Woods, K., Kegelmeyer, W.P., and Bowyer, K.: “Combination of multiple classifiers using local accuracyestimates”. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, 19(4), pp. 405–410.
G. Giacinto, F. Roli, and G. Fumera, Selection of image classifiers, Electronics Letters, vol. 36, no. 05, 2nd March 2000, pp. 420–422
Roli F., Multisensor image recognition by neural networks with understandable behaviour International Journal of Pattern Recognition and Artificial Intelligence 10 (1996) 887–917.
Giacinto G, Roli F., Bruzzone L, 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
G. Giacinto, F. Roli, and G. Fumera, Selection of Classifiers based on Multiple Classifier Behaviour, Proc. of the Joint international workshops on Syntactical and Structural Pattern Recognition & Statistical Pattern Recognition, Alicante, Spain, August 30/2–September 1, 2000, in press
R. A. Jacobs, M.I. Jordan, S.J. Nowlan and G.E. Hinton, “Adaptive Mixtures of Local Experts”, Neural computation 3, 1995, 79–87
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Giacinto, G., Roli, F. (2000). Dynamic Classifier Selection. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_17
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DOI: https://doi.org/10.1007/3-540-45014-9_17
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