Application of K-Nearest Neighbor Rule in the Case of Intuitionistic Fuzzy Sets for Pattern Recognition
In the present paper an algorithm 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. The choice of the KNN ru...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Academic Publishing House
2009-12-01
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| Series: | Bioautomation |
| Subjects: | |
| Online Access: | http://www.clbme.bas.bg/bioautomation/2009/vol_13.4/files/13.4_5.02.pdf |
| Summary: | In the present paper an algorithm 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. The choice of the KNN rule is due to the high precision of the method in decision making for pattern recognition problems, while the apparatus of the intuitionistic fuzzy sets is used to describe more adequately the considered objects and allows for pattern recognition with non-strict membership of the patterns. |
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| ISSN: | 1313-261X 1312-451X |