Tendency Coefficient-Based Weighted Distance Measure for Intuitionistic Fuzzy Sets with Applications

Decision-making (DM) as a critical aspect of every human endeavor has greatly been challenged by the presence of uncertainties and imprecisions. Albeit, the concept of intuitionistic fuzzy sets (IFSs) has been of great help in resolving uncertainties and imprecisions in DM via distance operators. Many authors have constructed sundry distance measuring techniques under IFSs, but none of them have considered the tendency coefficients of the intuitionistic fuzzy parameters and the weights of the elements upon which the IFSs are defined. In this paper, a new distance measure between IFSs is constructed, which we call tendency coefficient-based weighted distance measure (TCWDM). Besides the fact that tendency coefficients and weights are considered, the complete defining parameters of IFSs are also put into account to forestall error of exclusion during the distance computation. After characterizing the TCWDM under IFSs to ensure its alliance with the distance metric, the applications of the new distance operator is discussed in problems pertaining pattern recognition and disease diagnosis. In addition, we present a comparative study of the TCWDM alongside other extant distance measuring techniques under IFSs, and observe that the new approach is consistent with the distance metric, yields more reasonable results, and offers reliable interpretations in real-world applications.