Vector valued similarity measures for Atanassov's intuitionistic fuzzy sets

Beliakov, G, Pagola, M and Wilkin, T 2014, Vector valued similarity measures for Atanassov's intuitionistic fuzzy sets, Information sciences, vol. 280, pp. 352-367, doi: 10.1016/j.ins.2014.04.056.

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Title Vector valued similarity measures for Atanassov's intuitionistic fuzzy sets
Author(s) Beliakov, GORCID iD for Beliakov, G orcid.org/0000-0002-9841-5292
Pagola, M
Wilkin, TORCID iD for Wilkin, T orcid.org/0000-0003-4059-1354
Journal name Information sciences
Volume number 280
Start page 352
End page 367
Publisher Elsevier Inc.
Place of publication Philadelphia, PA
Publication date 2014-10-01
ISSN 0020-0255
1872-6291
Keyword(s) Atanassov intuitionistic fuzzy entropy
Atanassov's intuitionistic fuzzy set
similarity measure
science and technology
technology
computer science, information systems
restricted equivalence functions
pattern-recognition
entropy
fuzziness
operators
family
Summary We present a new approach for defining similarity measures for Atanassov's intuitionistic fuzzy sets (AIFS), in which a similarity measure has two components indicating the similarity and hesitancy aspects. We justify that there are at least two facets of uncertainty of an AIFS, one of which is related to fuzziness while other is related to lack of knowledge or non-specificity. We propose a set of axioms and build families of similarity measures that avoid counterintuitive examples that are used to justify one similarity measure over another. We also investigate a relation to entropies of AIFS, and outline possible application of our method in decision making and image segmentation. © 2014 Elsevier Inc. All rights reserved.
Language eng
DOI 10.1016/j.ins.2014.04.056
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067762

Document type: Journal Article
Collections: School of Information Technology
2018 ERA Submission
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