Optimizing the error/reject trade-off for a multi-expert system using the Bayesian combining rule

  • L. P. Cordella
  • P. Foggia
  • C. Sansone
  • F. Tortorella
  • M. Vento
Rejection in Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

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.

Keywords

Recognition Rate Input Sample Reliability Parameter Misclassification Rate Learn Vector Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • L. P. Cordella
    • 1
  • P. Foggia
    • 1
  • C. Sansone
    • 1
  • F. Tortorella
    • 1
  • M. Vento
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaNapoliItaly

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