Imprecise information in Bayes classifier

Abstract

The paper considers the problem of classification error in pattern recognition. This model of classification is primarily based on the Bayes rule and secondarily on the notion of intuitionistic or interval-valued fuzzy sets. A probability of misclassifications is derived for a classifier under the assumption that the features are class-conditionally statistically independent, and we have intuitionistic or interval-valued fuzzy information on object features instead of exact information. A probability of the intuitionistic or interval-valued fuzzy event is represented by the real number. Additionally, the received results are compared with the bound on the probability of error based on information energy. Numerical example concludes the work.

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Acknowledgments

This work is supported by The Polish Ministry of Science and Higher Education under the grant which is being realized in years 2010–2013.

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Correspondence to Robert Burduk.

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Burduk, R. Imprecise information in Bayes classifier. Pattern Anal Applic 15, 147–153 (2012). https://doi.org/10.1007/s10044-011-0201-6

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Keywords

  • Bayes decision rule
  • Imprecise observations
  • Probability of error