Abstract
Recently, in the framework of Pattern Recognition, methods for combining several experts (Multi-Expert Systems, MES) in order to improve the recognition performance, have been widely investigated. A main problem of MES is that the combining rule should be able to take the right classification decision even when the experts disagree. Anyway, in critical cases, a reject decision is convenient to reduce the risk of an error. Up to now, the problem of defining a reject rule for a MES has not been systematically explored.
We propose a method for determining the best trade-off between error rate and reject rate depending on the considered application domain, i.e. by taking into account the costs attributed, for the specific application, to misclassifications, rejects and correct classifications. Even though the method has general validity, in this paper its application to a MES using the Bayesian combining rule is presented.
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References
C.Y. Suen, C. Nadal, R. Legault, T.A. Mai, and L. Lam, “Computer Recognition of Unconstrained Handwritten Numeral”, Proceedings of the IEEE, vol. 80 no. 7, pp. 1162–1180, 1992.
B. Ackermann, H. Bunke, “Combination of Classifiers on the Decision Level for Face Recognition”, Technical Report IAM-96-002, Institut für Informatik and angewandte Mathematik, Universität Bern, January 1996.
T.K. Ho, J.J. Hull, S.N. Srihari, “Decision Combination in Multiple Classifier Systems”, IEEE Transactions on PAMI, vol. 16, n. 1, pp. 66–75, 1994.
L. Xu, A. Krzyzak, C.Y. Suen, “Method of Combining Multiple Classifiers and Their Application to Handwritten Numeral Recognition”, IEEE Trans. on SMC, vol. 22, no. 3, pp. 418–435, 1992.
Y.S. Huang, C.Y. Suen, “A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals”, IEEE Trans. on PAMI, vol. 17, no. 1, pp. 90–94, 1995.
L.P. Cordella, C. De Stefano, F. Tortorella, M. Vento, “A Method for Improving Classification Reliability of Multilayer Perceptrons” IEEE Trans. on Neural Networks, vol. 6, no. 5, pp. 1140–1147, 1995.
C. K. Chow, “On Optimum Recognition Error and Reject Tradeoff”, IEEE Trans. on Information Theory, vol. 16, no. 1, January 1970.
M.D. Richard, R.P. Lippman, “Neural Network Classifiers Estimates Bayesian a posteriori Probabilities”, Neural Computation, vol. 3, no. 4, pp. 461–483, 1991.
I. Bloch, “Information Combination Operators for Data Fusion: A Comparative Review with Classification”, IEEE Trans. on SMC — Part A, vol. 26, no. 1, pp. 52–76, 1996.
W.K. Pratt, Digital Image Processing, Wiley-Interscience, New York, 1991.
L.P. Cordella, C. De Stefano, M. Vento, “A Neural Network Classifier for OCR Using Structural Descriptions”, Machine Vision and Applications, no. 8, pp. 336–342, 1995.
P. Foggia, C. Sansone, F. Tortorella, and M. Vento, “Character Recognition by Geometrical Moments on Structural Decompositions”, in Proc. of the 4 th ICDAR, Ulm (D), IEEE Computer Society Press, pp. 6–10, 1997.
T.M. Cover, P.E. Hart, “Nearest Neighbor Pattern Classification”, IEEE Transactions on Information Theory, vol. 13, pp. 21–27, 1967.
D.E. Rumelhart, J.L. Mc Clelland, Parallel Distributed Processing — Explorations in the Microstructure of Cognition, Vol.1: Foundations, MIT Press, Cambridge, Mass, 1986.
T. Kohonen, “The Self-Organizing Map”, Proceedings of the IEEE, vol. 78, no. 9, pp. 1464–1480, 1990.
R. Hecth-Nielsen, Neurocomputing, Addison-Wesley, Reading (MA), 1990.
C. De Stefano, P. Foggia, F. Tortorella, M. Vento, “A Distance Measure for Structural Descriptions Using Circle Arcs as Primitives” in Proc. of the 13 th ICPR, Vienna, IEEE Computer Society Press, Vol. II, pp. 290–294, 1996.
P.J. Grother, “NIST Special Database 19”, Technical Report, National Institute of Standards and Technology, 1995.
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Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M. (1998). Optimizing the error/reject trade-off for a multi-expert system using the Bayesian combining rule. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033296
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DOI: https://doi.org/10.1007/BFb0033296
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