Biblioteca de la Universidad Complutense de Madrid

Determining the accuracy in supervised fuzzy classification problems

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Gómez, Daniel y Montero, Javier (2008) Determining the accuracy in supervised fuzzy classification problems. In Computational intelligence in decision and control : proceedings of the 8th International FLINS Conference. World Scientific, Singapore, pp. 411-416. ISBN 978-981-279-946-3

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URL Oficial: http://eproceedings.worldscinet.com/9789812799470/9789812799470_0067.html



Resumen

A large number of accuracy measures for image classification are actually available in the literature for cris classification. Overall accuracy, producer accuracy, user accuracy, kappa index and tau value are some examples. But in contrast to this effort in measuring the accuracy in a crisp framework, few proposals can be found in order to determine accuracy for soft classifiers. In this paper we define some accuracy measures for soft classification that extend some classical accuracy measures for crisp classifiers. This elms of measures takes into account the preferences of the decision maker in order to differentiate some errors that in practice may not be have same relevance.


Tipo de documento:Sección de libro
Información Adicional:

8th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science.
SEP 21-24, 2008

Palabras clave:Kappa
Materias:Ciencias > Informática > Lenguajes de programación
Código ID:16913
Depositado:29 Oct 2012 11:01
Última Modificación:19 Abr 2016 14:51

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