Abstract
We introduce notions of soft rough m-polar fuzzy sets and m-polar fuzzy soft rough sets as novel hybrid models for soft computing, and investigate some of their fundamental properties. We discuss the relationship between m-polar fuzzy soft rough approximation operators and crisp soft rough approximation operators. We also present applications of m-polar fuzzy soft rough sets to decision-making.
Highlights
The notion of bipolar fuzzy sets was generalized to m-polar fuzzy sets by Chen et al [1] in 2014.Chen et al [1] proved that bipolar fuzzy sets and 2-polar fuzzy sets are cryptomorphic mathematical tools
In many real life complicated problems, data sometimes comes from n agents (n ≥ 2), that is, multipolar information exists
We prove that the mF soft rough approximation operators can be described by crisp soft rough approximation operators
Summary
The notion of bipolar fuzzy sets was generalized to m-polar fuzzy sets by Chen et al [1] in 2014. In 1982, Pawlak [7] introduced the idea of rough set theory, which is an important mathematical tool to handle imprecise, vague and incomplete information. Alcantud and Santos-Garcia [20,21] produced a completely new approach to soft set based decision-making problems when information is incomplete. They proposed and compared an algorithmic solution with previous approaches in the literature in [20]. The idea of m-polar fuzzy soft rough sets can be utilized to solve different real-life problems. We present a new method to decision-making based on m-polar fuzzy soft rough sets
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
