Title: A novel distance measure for intuitionistic fuzzy sets and their application in pattern classification, medical diagnosis, and career determination

Authors: Joydeep Potwary; Anjali Patel; Subhankar Jana; Juthika Mahanta

Addresses: Department of Mathematics, NIT Silchar, Silchar, Assam, 788010, India ' Department of Mathematics, NIT Silchar, Silchar, Assam, 788010, India ' Department of Mathematics, NIT Silchar, Silchar, Assam, 788010, India ' Department of Mathematics, NIT Silchar, Silchar, Assam, 788010, India

Abstract: This study investigates some existing distance measures for intuitionistic fuzzy sets (IFSs). Existing distance measures have several drawbacks such as division by zero, counterintuitive and unreasonable results, violation of the fundamental properties of intuitionistic fuzzy distance measures. So, this study introduces a novel distance measure for IFSs to calculate the difference between them in a quantitative way. This measure is constructed as an effective tool to differentiate IFSs with a slight change either in membership or in non-membership degrees. The proposed measure is analysed mathematically via various set-theoretic conditions. Numerical examples have been employed to demonstrate the superiority of the proposed distance measure in contrast with the previously prevalent distance measures. The efficiency of the proposed distance measure with the conventional measures in the fields of pattern classification has been portrayed. The suggested measure addresses the shortcomings of the previously existing methods. It proved to be very effective for many sorts of decision-making problems, such as career determination and disease diagnostics.

Keywords: IFS; intuitionistic fuzzy set; distance measure; pattern classification; medical diagnosis; career determination.

DOI: 10.1504/IJCSM.2024.137834

International Journal of Computing Science and Mathematics, 2024 Vol.19 No.3, pp.193 - 205

Received: 19 May 2022
Accepted: 27 Jul 2023
Published online: 05 Apr 2024 *

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