TY - JOUR
T1 - A New Representation of Intuitionistic Fuzzy Systems and Their Applications in Critical Decision Making
AU - Son, Le Hoang
AU - Ngan, Roan Thi
AU - Ali, Mumtaz
AU - Fujita, Hamido
AU - Abdel-Basset, Mohamed
AU - Giang, Nguyen Long
AU - Manogaran, Gunasekaran
AU - Priyan, M. K.
N1 - Funding Information:
R. T. Ngan would like to thank the Project 911 of VNU University of Science, Vietnam National University, for supporting her work. This work was supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant 102.05-2013.37.
Funding Information:
Gunasekaran Manogaran He is currently a Big Data Scientist with the University of California at Davis, Davis, CA, USA. He has authored/coauthored conference papers, book chapters, and journals. His current research interests include data mining, big data analytics, and soft computing. He received the B.Eng. degree from Anna University and the M.Tech. and Ph.D. degrees from the Vellore Institute of Technology, India. He was a Research Assistant for a project on spatial data mining funded by the Indian Council of Medical Research, Government of India. . He has been a member of the Program Committee for several international/national conferences and workshops. He is a member of the International Society for Infectious Diseases and the Machine Intelligence Research labs. He is also the Technical Program Committee for the 2018 IEEE International Conference on Consumer Electronics, Las Vegas, USA. He is the recipient of the Award for Young Investigator from India and Southeast Asia by the Bill and Melinda Gates Foundation, USA. He is on the Reviewer Board of several international journals. He is a Coinvestigator for the project entitled “Agent Based Modeling of HIV epidemic in state of Telangana, India” funded by Pitt Public Health, Pittsburgh University, USA. He is also a Guest Editor for various international journals, including IEEE, Springer, Elsevier, Inderscience, IGI, Taylor&Francis, and Emerald publishing. Contact him at: [email protected].
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - In decision-making problem, fuzzy system is considered as an effective tool with access to uncertain information by fuzzy representations. Evolutionary fuzzy systems have been developed with the appearance of intuitionistic fuzzy, hesitant fuzzy, neutrosophic representations, etc. Moreover, by capturing compound features and convey multifaceted information, complex numbers are utilized to generalize fuzzy and intuitionistic fuzzy sets. However, the order relations established in these existing systems have certain limitations, such as they are not total order relations or they are defined based on intermediate functions, hence it is difficult to use in building and ensuring important properties of logical operators and distance measures in the systems. In this article, a representation of the intuitionistic fuzzy systems based on complex numbers (IFS-C) in the polar form by a new way is proposed to overcome the above restrictions. Specifically, an intuitionistic fuzzy set is characterized by the two functions of modulus and argument. A new order relation, set-theoretic operations, and a new distance measure by the polar form of IFS-C are defined and investigated. The applicability of the proposal is illustrated by a new decision-making model called P-distance measure. It is tested on the benchmark medical datasets in comparison with the existing methods. The experiments confirm the advantages of the proposal.
AB - In decision-making problem, fuzzy system is considered as an effective tool with access to uncertain information by fuzzy representations. Evolutionary fuzzy systems have been developed with the appearance of intuitionistic fuzzy, hesitant fuzzy, neutrosophic representations, etc. Moreover, by capturing compound features and convey multifaceted information, complex numbers are utilized to generalize fuzzy and intuitionistic fuzzy sets. However, the order relations established in these existing systems have certain limitations, such as they are not total order relations or they are defined based on intermediate functions, hence it is difficult to use in building and ensuring important properties of logical operators and distance measures in the systems. In this article, a representation of the intuitionistic fuzzy systems based on complex numbers (IFS-C) in the polar form by a new way is proposed to overcome the above restrictions. Specifically, an intuitionistic fuzzy set is characterized by the two functions of modulus and argument. A new order relation, set-theoretic operations, and a new distance measure by the polar form of IFS-C are defined and investigated. The applicability of the proposal is illustrated by a new decision-making model called P-distance measure. It is tested on the benchmark medical datasets in comparison with the existing methods. The experiments confirm the advantages of the proposal.
KW - complex numbers
KW - decision making
KW - evolutionary fuzzy systems
KW - intuitionistic fuzzy set
KW - logical operations
UR - http://www.scopus.com/inward/record.url?scp=85071768124&partnerID=8YFLogxK
U2 - 10.1109/MIS.2019.2938441
DO - 10.1109/MIS.2019.2938441
M3 - Article
AN - SCOPUS:85071768124
SN - 1541-1672
VL - 35
SP - 6
EP - 17
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 1
M1 - 8821387
ER -