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Some Properties of Binary Classifier with Fuzzy-Valued Loss Function

  • Robert Burduk
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

In this paper we present some prosperities of binary classifier with fuzzyvalued loss function. The loss function in our case is dependent on the stage of the decision tree or depends on the node of the decision tree. The decision rules of a two-stage binary classifier minimize the mean risk, that is the mean value of the fuzzy loss function. In the paper the effect of a loss function on the value of the separation point of decision regions is presented. In this paper we will are not going to study the impact of the choice of ranking of fuzzy numbers method on the results of the classification.

Keywords

Decision Rule Loss Function Fuzzy Number Recognition Algorithm Recognition Problem 
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 2011

Authors and Affiliations

  • Robert Burduk
    • 1
  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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