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.