On the multistage Bayes classifier
Pattern Recognition
Neural Computation
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SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
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ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
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AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Fuzzy basis functions: comparisons with other basis functions
IEEE Transactions on Fuzzy Systems
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Pattern Recognition Letters
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Machine Vision and Applications
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International Journal of Biometrics
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We consider a popular approach to multicategory classification tasks: a two-stage system based on a first (global) classifier with rejection followed by a (local) nearest-neighbor classifier. Patterns which are not rejected by the first classifier are classified according to its output. Rejected patterns are passed to the nearest-neighbor classifier together with the {\rm{top}}\hbox{-}h ranking classes returned by the first classifier. The nearest-neighbor classifier, looking at patterns in the {\rm{top}}\hbox{-}h classes, classifies the rejected pattern. An editing strategy for the nearest-neighbor reference database, controlled by the first classifier, is also considered. We analyze this system, showing that even if the first level and nearest-neighbor classifiers are not optimal in a Bayes sense, the system as a whole may be optimal. Moreover, we formally relate the response time of the system to the rejection rate of the first classifier and to the other system parameters. The error-response time trade-off is also discussed. Finally, we experimentally study two instances of the system applied to the recognition of handwritten digits. In one system, the first classifier is a fuzzy basis functions network, while in the second system it is a feed-forward neural network. Classification results as well as response times for different settings of the system parameters are reported for both systems.