Optimized data manipulation methods for intensive hesitant fuzzy set with applications to decision making

Abstract

The emergence of large amounts of hesitant fuzzy data brings more opportunities and challenges for optimal decision-making results. The granularity of the hesitant fuzzy set has been significantly improved, but the potential for more inconsistent data also increases. Due to high computational complexity and low efficiency, the existing data aggregation methods cannot handle the intensive hesitant fuzzy data. To solve this problem, we first propose the optimization strategy for the intensive hesitant fuzzy data. Then the data redundancy elimination method and the data aggregation method are proposed. The extensions of hesitant fuzzy set and normal-type hesitant fuzzy set are proposed for intensive hesitant fuzzy data manipulations. The aggregation methods and the data structure estimation methods of the extensions are also discussed. Finally, a practical application to the wine quality determination is provided, and some comparative analyses are conducted to demonstrate the effectiveness of the proposed methods.