An improved cosine similarity measure for intuitionistic fuzzy sets and their applications to decision-making process

In the present study, improved cosine similarity measure for an intuitionistic fuzzy sets (IFSs) has been proposed by considering the interaction between the pairs of the membership degrees. Pairs of membership, non-membership are to be considered as vector representation during the formulation. The shortcomings of the existing measures have been highlighted and overcome by using the proposed measure. Also, in order to deal with the situation where the elements in a set are correlative, weighted cosine similarity measure has been defined. Finally, multi-criteria decision making (MCDM) method, based on the proposed similarity measure, has been presented under intuitionistic fuzzy environment. Numerical examples, one from the investment the money and others from the pattern recognition and medical diagnosis, have been taken to demonstrate the efficiency of the proposed approach and compared their results with the existing approaches results.