On a generalized entropy and dissimilarity measure in intuitionistic fuzzy environment with applications

Imprecision is an intrinsic characteristic of human behaviour. The concept of fuzziness is a well-recognized mechanism to quantify the imprecision and ambiguity of human judgement. In the last three decades, various extensions of fuzzy theory have been put forward. Intuitionistic fuzzy theory reveals some interesting aspects of approximate models. Intuitionistic fuzzy entropy measures the average amount of imprecision and ambiguity present in an intuitionistic fuzzy set. In this work, we propose flexible (generalized) measure of intuitionistic fuzzy (IF) entropy and establish its superiority over some existing IF-entropies. We introduce a generalized IF-dissimilarity measure and prove some of its properties. We also provide an improved TOPSIS method to handle multiple attribute group decision-making problem in intuitionistic fuzzy settings using our proposed IF-entropy, IF-dissimilarity and IF-correlation efficiency. We then investigates the performance of the proposed IF-dissimilarity in a pattern recognition problem and obtain encouraging results.