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A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition problems
Author(s) -
Hatzimichailidis A.G.,
Papakostas G.A.,
Kaburlasos V.G.
Publication year - 2012
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21529
Subject(s) - measure (data warehouse) , distance measures , distance matrix , mathematics , fuzzy logic , fuzzy set , flexibility (engineering) , computer science , artificial intelligence , pattern recognition (psychology) , matrix (chemical analysis) , set (abstract data type) , data mining , algorithm , statistics , materials science , composite material , programming language
Abstract A novel distance measure between two intuitionistic fuzzy sets (IFSs) is proposed in this paper. The introduced measure formulates the information of each set in matrix structure, where matrix norms in conjunction with fuzzy implications can be applied to measure the distance between the IFSs. The advantage of this novel distance measure is its flexibility, which permits different fuzzy implications to be incorporated by extending its applicability to several applications where the most appropriate implication is used. Moreover, the proposed distance might be expressed equivalently by using either intuitionistic fuzzy sets or interval‐valued fuzzy sets. Appropriate experimental configurations have taken place to compare the proposed distance measure with similar distance measures from the literature, by applying them to several pattern recognition problems. The results are very promising because the performance of the new distance measure outperforms the corresponding performance of well‐known IFSs measures, by recognizing the patterns correctly and with high degree of confidence. © 2012 Wiley Periodicals, Inc.