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Issue:Generalized net model of a k-nearest neighbor rule pattern recognition algorithm for the case of intuitionistic fuzzy sets

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Title of paper: Generalized net model of a k-nearest neighbor rule pattern recognition algorithm for the case of intuitionistic fuzzy sets
Author(s):
Peter Vassilev
Centre of Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., 1113 Sofia, Bulgaria
peter.vassilev@gmail.com
Ludmila Todorova
Centre of Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., 1113 Sofia, Bulgaria
lpt@clbme.bas.bg
Presented at: 10th IWGN, Sofia, 5 December 2009
Published in: Conference proceedings, pages 8—13
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Abstract: In the present paper a generalized net model of an algorithm for pattern classification based on the k-nearest neighbors (KNN) rule modified for the case of intuitionistic fuzziness is proposed. The algorithm calculates the degrees of membership, non-membership and indeterminacy for each new element that needs to be classified.
Keywords: Generalized nets, k-nearest neighbors rule, Intuitionistic fuzzy sets, Pattern recognition.
References:
  1. Todorova, L., Vassilev P. (2009) Application of K-Nearest Neighbor Rule in the Case of Intuitionistic Fuzzy Sets for Pattern Recognition, BIOautomation, 13(4), 265-270
  2. Tou, J., Gonzalez R. (1974) Pattern Recognition Principles. Massachusetts: Addison-Wesley Publishing Company.
  3. Atanassov K. (1999) Intuitionistic Fuzzy Sets. Heidelberg: Physica-Verlag.
  4. Atanassov, K. (2007) On Generalized Nets Theory, Academic Publishing House “Prof. M. Drinov”, Sofia.
  5. Atanassov, K. (1992) Introduction to the Theory of Generalized Nets. Pontika-Print Press, Burgas (in Bulgarian).
  6. Atanassov, K. (1997) Generalized Nets and System Theory. Academic Publishing House “Prof. M. Drinov”, Sofia.
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