The propagation and combination of uncertainties can significantly influence the performance of an information system. Although there have been great efforts in the propagation of randomness, fuzziness and other uncertainties, there is not yet a thorough study for the propagation of roughness in rough set operations. The derivation of a roughness bound for various rough set operations is given. We proved that there is no defined bound for the intersection operation of any two rough sets but there are bounds for union, complement and most difference operations. This is beneficial for decision making involving large volumes of rough set operations
Rough set theory provides a useful mathematical foundation for developing automated computational sy...
An integration between the theories of fuzzy sets and rough sets has been attempted by providing a m...
The Rough Set Theory was proposed by Pawlak, in 1982, as a mathematical model to represent knowledge...
This paper presents some roughness bounds for rough set operations. The results show that a bound of...
AbstractThis paper investigates the general roughness bounds for rough set operations. Compared with...
Abstract. Rough sets have traditionally been applied to decision (classification) problems. We sugge...
The original rough set theory deals with precise and complete data, while real applications frequent...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...
AbstractIn rough set theory, the accuracy measure is an important numerical characterization that qu...
AbstractRough set theory, initiated by Pawlak, is a mathematical tool in dealing with inexact and in...
AbstractRough set theory is a relatively new mathematical tool for use in computer applications in c...
Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be consider...
AbstractProbabilistic approaches have been applied to the theory of rough set in several forms, incl...
Abstract -Knowledge acquisition under uncertainty using rough set theory was first stated as a conce...
Rough set theory is a new mathematical approach to vagueness and uncertainty. The theory has found m...
Rough set theory provides a useful mathematical foundation for developing automated computational sy...
An integration between the theories of fuzzy sets and rough sets has been attempted by providing a m...
The Rough Set Theory was proposed by Pawlak, in 1982, as a mathematical model to represent knowledge...
This paper presents some roughness bounds for rough set operations. The results show that a bound of...
AbstractThis paper investigates the general roughness bounds for rough set operations. Compared with...
Abstract. Rough sets have traditionally been applied to decision (classification) problems. We sugge...
The original rough set theory deals with precise and complete data, while real applications frequent...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...
AbstractIn rough set theory, the accuracy measure is an important numerical characterization that qu...
AbstractRough set theory, initiated by Pawlak, is a mathematical tool in dealing with inexact and in...
AbstractRough set theory is a relatively new mathematical tool for use in computer applications in c...
Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be consider...
AbstractProbabilistic approaches have been applied to the theory of rough set in several forms, incl...
Abstract -Knowledge acquisition under uncertainty using rough set theory was first stated as a conce...
Rough set theory is a new mathematical approach to vagueness and uncertainty. The theory has found m...
Rough set theory provides a useful mathematical foundation for developing automated computational sy...
An integration between the theories of fuzzy sets and rough sets has been attempted by providing a m...
The Rough Set Theory was proposed by Pawlak, in 1982, as a mathematical model to represent knowledge...