A note on “A novel correlation coefficient of intuitionistic fuzzy sets based on the connection number of set pair analysis and its application”
Akanksha Singha112Corresponding author & Current Address: Lecturer, School of Sciences, Baddi University of Emerging Sciences and Technologies, Makhnumajra, Baddi district, Solan Baddi, Himachal Pradesh, IN 173205 Email id: akanksha.singh@baddiuniv.ac.in ORCID ID: 0000-0003-2189-4974 Phone no.: +91-98884146112, Shahid Ahmad Bhata3
aSchool of Mathematics,
Thapar Institute of Engineering & Technology (Deemed to be University)
P.O. Box 32, Patiala, Pin -147004, Punjab, India
asingh3_phd16@thapar.edu1, bhatshahid444@gmail.com3
Abstract: Garg and Kumar (Scientia Iranica, 2017, https://doi.org/ 10.24200/SCI.2017.4454) proposed some new correlation coefficient between intuitionistic fuzzy sets (IFSs). To point out the advantages of their proposed correlation coefficient over the existing correlation coefficient, Garg and Kumar applied their proposed correlation coefficient as well as the existing correlation coefficient to identify a suitable classifier for an unknown pattern, represented by an intuitionistic fuzzy set (IFS), from the known patterns, each represented by IFS. Garg and Kumar suggested that the existing correlation coefficient fails to identify a suitable classifier, whereas, the correlation coefficient, proposed by them, does not fail to identify a suitable classifier. So, it is appropriate to use the correlation coefficient, proposed by them, instead of the existing correlation coefficient. In this note, it is shown that the correlation coefficient, proposed by Garg and Kumar, also fails to identify a suitable classifier. Furthermore, it is shown that more computational efforts are required to apply the correlation coefficient, proposed by Garg and Kumar, as compared to the existing correlation coefficient. In the actual case, it is inappropriate to apply the correlation coefficient for identifying a suitable classifier.
Keywords:Set pair analysis; Connection number (CN); IFS; Pattern recognition; Medical diagnosis; Decision making.