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Classification error in Bayes multistage recognition task with fuzzy observations

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Abstract

The paper considers the problem of classification error in multistage pattern recognition. This model of classification is based primarily on the Bayes rule and secondarily on the notion of fuzzy numbers. In adopting a probability-fuzzy model two concepts of hierarchical rules are proposed. In the first approach the local criterion that denote the probabilities of misclassification for particular nodes of a tree is considered. In the second approach the global optimal strategy that minimises the mean probability of misclassification on the whole multistage recognition process is considered. A probability of misclassifications is derived for a multiclass hierarchical classifier under the assumption that the features at different nodes of the tree are class-conditionally statistically independent, and we have fuzzy information on object features instead of exact information. Numerical example of this difference concludes the work.

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Acknowledgments

This work is supported by The Polish State Committee for Scientific Research under grant for 2006–2009.

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  1. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wrocław, Poland

    Robert Burduk

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  1. Robert Burduk
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Correspondence to Robert Burduk.

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Burduk, R. Classification error in Bayes multistage recognition task with fuzzy observations. Pattern Anal Applic 13, 85–91 (2010). https://doi.org/10.1007/s10044-008-0143-9

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  • DOI: https://doi.org/10.1007/s10044-008-0143-9

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