Data analysis using the maximum probabilistic rough set in the R environment

To address data analysis problems in practice, numerous researchers have developed several models, yet it might be difficult to reduce data effectively without losing the original information. This paper proposes a new concept of a non-parametric model that achieves the best attribute reduction without removing highly significant attributes. In this paper, the concept of maximum probabilistic rough set (MPRS) is introduced and its properties are discussed where it has been found that the positive region in MPRS is a superset of rough set (RS). Later, the implementation of MPRS is put forward to solve real-life problems using R Language and it is compared with several existing methods to illustrate the advantages. Experimental results demonstrate that MPRS achieves better reduction without compromising the consistency factor.