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 Download:  PDF (239  Kb, File info) 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: 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 Tou, J., Gonzalez R. (1974) Pattern Recognition Principles. Massachusetts: Addison-Wesley Publishing Company. Atanassov K. (1999) Intuitionistic Fuzzy Sets. Heidelberg: Physica-Verlag. Atanassov, K. (2007) On Generalized Nets Theory, Academic Publishing House “Prof. M. Drinov”, Sofia. Atanassov, K. (1992) Introduction to the Theory of Generalized Nets. Pontika-Print Press, Burgas (in Bulgarian). Atanassov, K. (1997) Generalized Nets and System Theory. Academic Publishing House “Prof. M. Drinov”, Sofia. Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.