An algorithmic robot selection method for incomplete rough fuzzy set
Seema Singh, Dhara Singh Hooda · Research in Statistics · 2023
Missing data exist in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. However, collecting data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.