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Pattern Analysis and Applications

, Volume 9, Issue 4, pp 353–358 | Cite as

Two-stage binary classifier with fuzzy-valued loss function

  • Robert BurdukEmail author
  • Marek Kurzyński
Short Paper

Abstract

In this paper we present the decision rules of a two-stage binary Bayesian classifier. The loss function in our case is fuzzy-valued and is dependent on the stage of the decision tree or on the node of the decision tree. The decision rules minimize the mean risk, i.e., the mean value of the fuzzy loss function. The model is first based on the notion of fuzzy random variable and secondly on the subjective ranking of fuzzy number defined by Campos and González. In this paper also, influence of choice of parameter λ in selected comparison fuzzy number method on classification results are presented. Finally, an example illustrating the study developed in the paper is considered.

Keywords

Two-stage binary classifier Decision rules Fuzzy loss function 

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Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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