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Atanassov’s Intuitionistic Fuzzy Sets as a Classification Model

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 4529))

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Abstract

In this paper we show that Atanassov’s Intuitionistic Fuzzy sets can be viewed as a classification model, that can be generalized in order to take into account more classes than the three classes considered by Atanassov’s (membership, non-membership and non-determinacy). This approach will imply, on one hand, to change the meaning of these classes, so each one will have a positive definition. On the other hand, this approach implies the possibility of a direct generalization for alternative logics and additional valuation states, being consistent with Atanassov’s focuss. From this approach we shall stress the absence of any structure within those three valuation states in Atanassov’s model. In particular, we consider this is the main cause of the dispute about Atanassov’s model: acknowledging that the nameintuitionistic is not appropriate, once we consider that a crisp direct graph is defined in the valuation space, formal differences with other three-state models will appear.

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Author information

Authors and Affiliations

  1. Facultad de Matemáticas, Universidad Complutense, Madrid, Spain

    J. Montero

  2. Escuela de Estadística, Universidad Complutense, Madrid, Spain

    D. Gómez

  3. Automática y Computación, Universidad Pública de Navarra, Pamplona, Spain

    H. Bustince

Authors
  1. J. Montero

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  2. D. Gómez

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  3. H. Bustince

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Editor information

Patricia Melin Oscar Castillo Luis T. Aguilar Janusz Kacprzyk Witold Pedrycz

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© 2007 Springer Berlin Heidelberg

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Montero, J., Gómez, D., Bustince, H. (2007). Atanassov’s Intuitionistic Fuzzy Sets as a Classification Model. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_7

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