Abstract
Combining classifiers is an effective way of improving classification performance. In many situations it is possible to construct several classifiers with different characteristics. Selecting the member classifiers with the best individual performance can be shown to be suboptimal in several cases, and hence there exists a need to attempt to find effective member classifier selection methods. In this paper six selection criteria are discussed and evaluated in the setting of combining classifiers for isolated handwritten character recognition. A criterion focused on penalizing many classifiers making the same error, the exponential error count, is found to be able to produce the best selections.
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References
Ho, T.K.: Multiple Classifier Combiation: Lessons and Next Steps. In: Hybrid Methods in Pattern Recognition. World Scientific Press (2002)
Kuncheva, L., Whittaker, C., Shipp, C., Duin, R.: Is independence good for combining classifiers. In: Proceedings of the 15th ICPR. Volume 2. (2000) 168–171
Kang, H., Lee, S.: An information-theoretic strategy for constructing multiple classifier systems. In: Proceedings of the 15th ICPR. Volume 2. (2000) 483–486
Roli, F., Giacinto, G.: Design of Multiple Classifier Systems. In: Hybrid Methods in Pattern Recognition. World Scientific Press (2002)
Huang, Y., Suen, C.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 90–94
Laaksonen, J., Aksela, M., Oja, E., Kangas, J.: Dynamically Expanding Context as committee adaptation method in on-line recognition of handwritten latin characters. In: Proceedings of ICDAR99. (1999) 796–799
Kohonen, T.: Dynamically expanding context. Journal of Intelligent Systems 1 (1987) 79–95
Vuori, V., Laaksonen, J., Oja, E., Kangas, J.: Experiments with adaptation strategies for a prototype-based recognition system of isolated handwritten characters. International Journal of Document Analysis and Recognition 3 (2001) 150–159
Sankoff, D., Kruskal, J.B.: Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. Addison-Wesley (1983)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines version 2.36. http://www.csie.ntu.edu.tw/cjlin/libsvm/ (2002)
Zell, A., Mache, N., et al, G.M.: Snns: Stuttgart neural network simulator. http://www-ra.informatik.uni-tuebingen.de/SNNS/ (2002)
Fumera, G., Roli, F.: Performance analysis and comparison of linear combiners for classifier fusion. In: Proceeding of S+SSPR2002. (2002) 424–432
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Aksela, M. (2003). Comparison of Classifier Selection Methods for Improving Committee Performance. 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_9
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DOI: https://doi.org/10.1007/3-540-44938-8_9
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