TY - GEN
T1 - Method Selecting Correct One among Alternatives Utilizing Intuitionistic Fuzzy Preference Relation Without Consensus Reaching Process
AU - Zhang, Hengshan
AU - Zheng, Qinghua
AU - Wang, Zhongmin
AU - Chen, Yanping
AU - Liu, Ting
AU - Chen, Tianhua
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - The methods with consensus reaching process can obtain a collective solution which is supported by most of decision makers in larger-scale group decision making. However, in case decision makers who could give correct opinions are from the minority, the conventional methods with consensus reaching process can not obtain the correct answer. In this paper, a novel method is developed to tackle this challenge. The decision makers give the opinions utilizing pairwise comparisons of the alternatives from positive and negative views based on intuitionistic fuzzy preference relation. The obtained opinions are translated into intuitionistic fuzzy numbers, and are further grouped and aggregated according to the alternatives. Based on the aggregated intuitionistic fuzzy numbers, the prediction normalized rate is defined and calculated for each alternative, the alternative with the minimal prediction normalized rate is selected as correct one. The experimental results show that the proposed method can obtain the correct answer even when the actual correct opinions are reflected by a small number of decision makers.
AB - The methods with consensus reaching process can obtain a collective solution which is supported by most of decision makers in larger-scale group decision making. However, in case decision makers who could give correct opinions are from the minority, the conventional methods with consensus reaching process can not obtain the correct answer. In this paper, a novel method is developed to tackle this challenge. The decision makers give the opinions utilizing pairwise comparisons of the alternatives from positive and negative views based on intuitionistic fuzzy preference relation. The obtained opinions are translated into intuitionistic fuzzy numbers, and are further grouped and aggregated according to the alternatives. Based on the aggregated intuitionistic fuzzy numbers, the prediction normalized rate is defined and calculated for each alternative, the alternative with the minimal prediction normalized rate is selected as correct one. The experimental results show that the proposed method can obtain the correct answer even when the actual correct opinions are reflected by a small number of decision makers.
KW - Consensus measure
KW - Consensus reaching process
KW - Intuitionistic Fuzzy Numbers
KW - Intuitionistic fuzzy preference relation
KW - Prediction normalized rate
UR - https://www.scopus.com/pages/publications/85073806580
U2 - 10.1109/FUZZ-IEEE.2019.8858956
DO - 10.1109/FUZZ-IEEE.2019.8858956
M3 - 会议稿件
AN - SCOPUS:85073806580
T3 - IEEE International Conference on Fuzzy Systems
BT - 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
Y2 - 23 June 2019 through 26 June 2019
ER -