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An adaptive reject option for LVQ classifiers

  • L. P. Cordella
  • C. De Stefano
  • C. Sansone
  • M. Vento
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 974)

Abstract

A reject rule devised for a neural classifier based on the Learning Vector Quantization (LVQ) paradigm is presented. The reject option is carried out adaptively to the specific application domain. It is assumed that a performance function P is defined which, taking into account the requirements of a given application expressed in terms of classification, misclassification and reject costs, evaluates the quality of the classification. Under this assumption the optimal reject threshold value, determining the best trade-off between reject rate and misclassification rate, is the one for which the function P reaches its absolute maximum. Implementation and performance of the rule are illustrated.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • L. P. Cordella
    • 1
  • C. De Stefano
    • 1
  • C. Sansone
    • 1
  • M. Vento
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversité di NapoliNapoliItaly

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