ICSES Transactions on Neural and Fuzzy Computing
Vol. 2, No. 2, Jun. 2019
An Overview on Hesitant Fuzzy Information Measures | Short Letter
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a Quchan University of Technology, Quchan, Iran
Prof. Bahram Farhadinia
Corresponding Author Affiliation: Quchan University of Technology, Quchan, Iran Tel: +989155280519 E-mail: bfarhadinia@qiet.ac.ir 2nd e-mail: bahramfarhadinia@yahoo.com Prof. Bahram Farhadinia's publications in ICSES
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This article has been retracted by International Computer Science and Engineering Society (ICSES) because of ethical misconduct, scientific distortion, or administrative error, and cannot be downloaded and used for any purpose based on the violation in ICSES Ethics in Publicationcall_made |
Retraction Note by the Editor-in-Chief
Highlights and Novelties
1- We are going to give a thorough and systematic review of distance measures for HFSs.
2- We are going to give a thorough and systematic review of similarity measures for HFSs.
3- We are going to give a thorough and systematic review of entropy measures for HFSs.
Manuscript Abstract
Although the concept of fuzzy set (FS) has been widely and successfully applied in many different areas to model some types of uncertainty, the limitation of this concept is still more serious in case of dealing with imprecise and vague information when different sources of vagueness appear simultaneously. Due to this fact and to overcome such limitations, a number of extensions of FSs have been introduced in the literature. By the way, among the most known extensions of FSs, hesitant fuzzy set (HFS) has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as decision making, aggregation operators, and information measures. Because of such a growth, throughout the present manuscript, we are going to give a thorough and systematic review to the main research results in the field of information measures for HFSs including the distance measures, the similarity measures, and the entropy measures. What seems more considerable in this study is the systematic transformation of the distance measure into the similarity measure and vice versa, and moreover, the two categories of entropy measures including those are derived from the other information measures, and those are based on axiomatic frameworks.
Keywords
Hesitant fuzzy set Distance measure Similarity measure Entropy measure
Copyright and Licence
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors. This manuscript is published in Open-Access manner based on the copyright licence of Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0).
Cite this manuscript as
Bahram Farhadinia, "An Overview on Hesitant Fuzzy Information Measures," ICSES Transactions on Neural and Fuzzy Computing, vol. 2, no. 2, pp. 22-27, Jun. 2019.
For External Scientific Databeses
--BibTex--
@article{al._288 title="An Overview on Hesitant Fuzzy Information Measures", author="Bahram Farhadinia", journal="ICSES Transactions on Neural and Fuzzy Computing (ITNFC)", volume="2", number="2", pages="22-27", year="2019", month="06", day="30", publisher= "International Computer Science and Engineering Society (ICSES)", doi="", url="http://www.i-cses.com/files/download.php?pID=288"}
--EndNote--
%0 Journal Article %T An Overview on Hesitant Fuzzy Information Measures %A Bahram Farhadinia %J ICSES Transactions on Neural and Fuzzy Computing (ITNFC) %V 2 %N 2 %P 22-27 %D 2019 %I International Computer Science and Engineering Society (ICSES) %U http://www.i-cses.com/files/download.php?pID=288 %8 2019-06-30 %R %@ 2717-0055
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