A Novel Similarity Measure Based on Accuracy Score of Conventional Type of Trapezoidal-Valued Intuitionistic Fuzzy Sets and Its Applications in Multi-criteria Decision-Making Problems

Abstract

The article aims to investigate the distance measure between any two conventional type trapezoidal-valued intuitionistic fuzzy sets (CTrVIFSs) whose membership and non-membership grades of an element are expressed as conventional trapezoidal intuitionistic fuzzy numbers (CTrIFN). Using the proposed distance measure, the similarity measure of CTrVIFSs is determined and its efficiency is shown by applying it to pattern recognition problems and MCDM problems. The similarity measure propounded in this article can be used to tackle real-world problems involving CTrVIFS as parameters, such as clustering, machine learning, and DNA matching. The application section discusses that this research can help decision-makers to recognize patterns and categorize samples with those patterns. Furthermore, the model of a real-world problem is given which utilizes the suggested similarity measure to solve MCDM problems, demonstrate the usability of the new technique and comprehend its applied intelligence above other methods. Finally, a general conclusion and future scope on this topic are discussed.