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Parameterized Imprecise Classification: Elicitation and Assessment

  • Isabela Drummond
  • Sandra Sandri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)

Abstract

This work is based on classifiers that can yield possibilistic valuations as output. The valuations may have been obtained from a labeled data set either directly as such, by possibilistic classifiers, by transforming the output of probabilistic classifiers or else by adapting prototype-based classifiers in general. Imprecise classifications are elicited from the possibilistic valuations by varying a parameter that makes the overall classification become more or less precise. We introduce some indices to assess the accuracy of the parameterized imprecise classifications and their reliability, thus allowing the user to choose the most suitable level of imprecision and/or uncertainty for a given application.

Keywords

Discriminant Function Confusion Matrice Possibility Distribution Possibility Theory Accuracy Index 
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 2006

Authors and Affiliations

  • Isabela Drummond
    • 1
  • Sandra Sandri
    • 2
  1. 1.Instituto Nacional de Pesquisas EspaciaisBrasil
  2. 2. BellaterraSpain

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