Abstract
AbstractThe necessity of appropriate divergence measures is arising as they play a vital role in different kinds of problems which are related to dissimilarity, inference, and discrimination. Intuitionistic fuzzy sets (IFSs) are very useful to manage the unassured state of data. For the evaluation of relationships of IFSs, divergence measures of IFSs are necessary. The information of each set in the matrix is formulated by the introduced intuitionistic fuzzy divergence measure, where the matrix under fuzzy environment is applied to find the divergence between the two IFSs. The main motive of this paper is to introduce a new generalized measure with proof of its validity. The proposed divergence measure is applied to the problems of medical diagnosis and pattern recognition on real-world datasets to examine the effectiveness and practicality. Also, the newly developed method is compared with the extant methods which is demonstrated in an intuitionistic fuzzy environment. It is noticed that the newly developed divergence measure found better results in comparison with the other existing methods.KeywordsIntuitionistic fuzzy setMedical diagnosisDivergence measurePattern recognitionDecision-making
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