Two-parametric generalized fuzzy knowledge measure and accuracy measure with applications

Fuzzy entropy concerning a fuzzy set computes the imprecision content of that set whereas the content of precision is evaluated by using the notion of fuzzy knowledge measure. In this paper, we suggest a two-parametric version of the knowledge measure in fuzzy settings. Further, we investigate its novelty and advantages from various viewpoints, such as attribute weight computation, ambiguity computation, and appropriate handling of the structured linguistic variables. We also introduce a two-parametric generalized fuzzy accuracy measure (GFAM) and demonstrate its application in pattern analysis. Additionally, we contrast the effectiveness of the suggested fuzzy accuracy measure with several existing compatibility measures. We also consider the Multiobjective Optimization on the basis of Ratio Analysis (MOORA) method of multiple attribute decision-making (MADM) based on the proposed two-parametric generalized fuzzy knowledge measure and two-parametric GFAM. We also contrast the MADM method with the conventional MOORA method.

Automated summary

This paper investigates knowledge measure and accuracy measure in fuzzy environments, exploring their applications in pattern analysis and decision-making. It compares the proposed measures with existing methods to evaluate their effectiveness and advantages.