Abstract
The q-rung orthopair fuzzy set is a recent development to study the ambiguous information present in a system. This is more powerful and comprehensive than the notion of fuzzy set, intuitionistic fuzzy set, and Pythagorean fuzzy set. The space of uncertain information described by q-rung orthopair fuzzy set is found to be more extensive and flexible due to the inclusion of the parameter q. The aim of the present study is to propose some new correlation coefficients for q-rung orthopair fuzzy sets. The existing correlation coefficients concerning q-rung orthopair fuzzy sets describe the extent of linear association between two q-rung orthopair fuzzy sets not the nature or direction. The proposed correlation coefficients determine degree as well as the nature of correlation (positive or negative) between two q-rung orthopair fuzzy sets. The proposed correlation coefficients receive their values in [− 1, 1]. We obtain the new correlation coefficients for q-rung orthopair fuzzy sets analogous to the crisp correlation coefficients. We expound the advantages of the proposed correlation coefficients using the notions of structured linguistic variables and degree of confidence. The comparative analysis of the proposed correlation coefficients with the existing non-standard fuzzy correlation/compatibility measures appropriately justifies the advantages. For the comparative analysis, we use synthetic as well as real data. The newly proposed measures show the greater degree of confidence in pattern classification and capture the linguistic variables more effectively. We also investigate the reasonability of the proposed correlation coefficients on the real data of the Iris plant. Moreover, we observe that the results of the proposed correlation coefficients in medical diagnosis are consistent with the existing compatibility measures. At last, we introduce a novel correlation-based closeness coefficient for solving a multi-attribute decision-making problem in q-rung orthopair fuzzy environment.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Ambient Intelligence and Humanized Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
