Double-quantitative $\gamma^{\ast}-$fuzzy coverings approximation operators

TL;DR

This paper introduces new $ ext{gamma}^*$-fuzzy covering approximation operators, establishing their relationships and demonstrating their application in constructing fuzzy set approximations with quantitative information.

Contribution

It develops novel $ ext{gamma}^*$-fuzzy covering approximation operators and explores their relationships, enhancing the bridge between fuzzy covering rough sets and Pawlak's model.

Findings

Introduced $ ext{gamma}^*$-fuzzy covering approximation operators.

Analyzed relationships among various $ ext{gamma}$-based approximation operators.

Provided examples illustrating the construction of fuzzy set approximations.

Abstract

In digital-based information boom, the fuzzy covering rough set model is an important mathematical tool for artificial intelligence, and how to build the bridge between the fuzzy covering rough set theory and Pawlak's model is becoming a hot research topic. In this paper, we first present the $\gamma-$fuzzy covering based probabilistic and grade approximation operators and double-quantitative approximation operators. We also study the relationships among the three types of $\gamma-$fuzzy covering based approximation operators. Second, we propose the $\gamma^{\ast}-$fuzzy coverings based multi-granulation probabilistic and grade lower and upper approximation operators and multi-granulation double-quantitative lower and upper approximation operators. We also investigate the relationships among these types of $\gamma-$fuzzy coverings based approximation operators. Finally, we employ several examples to illustrate how to construct the lower and upper approximations of fuzzy sets with the absolute and relative quantitative information.