Dominance-based Rough Set Approach (DRSA) has been proposed to deal with multi-criteria classification problems, where data may be inconsistent with respect to the dominance principle. However, in real-life datasets, in the presence of noise, the notions of lower and upper approximations handling inconsistencies were found to be excessively restrictive which led to the proposal of the variable ...

Many researchers have combined rough set theory and fuzzy set theory in order to easily approach problems of imprecision and uncertainty. Covering-based rough sets are one of the important generalizations of classical rough sets. Naturally, covering-based fuzzy rough sets can be studied as a combination of covering-based rough set theory and fuzzy set theory. It is clear that Pawlak’s rough set...

A ternary semigroup is a nonempty set together with a ternary multiplication which is associative. Any semigroup can be reduced to a ternary semigroup but a ternary semigroup does not necessarily reduce to a semigroup. The notion of fuzzy sets was introduced by Zadeh in 1965 and that of rough sets by Pawlak in 1982. Applications of the fuzzy set theory and rough set theory have been found in va...

In this article we discuss the connection of Rough Set methods and submodular functions. We show that discernibility measure used in reduct calculation is submodular and provides a bridge between certain methods from Rough Set theory and Submodular Function theory.

This model for fuzzy rough sets is one of the most important parts in rough set theory. Moreover, it is based on an equivalence relation (indiscernibility relation). However, many systems are not only concerned with fuzzy sets, but also based on a dominance relation because of various factors in practice. To acquire knowledge from the systems, construction of model for fuzzy rough sets based on...

Feature Selection (FS) methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the FS process with much success. More recently a method has been developed which uses fuzzy-entropy [9] to perform this task. Such use of fuzzy-entropy as an evaluation measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS a...