We show how definite extended logic programs can be used for defining and reason with rough sets. Moreover, a rough-set-specific query language is presented and an answering algorithm is outlined. Thus, we not only show a possible application of a paraconsistent logic to the field of rough sets as we also establish a link between rough set theory and logic programming, making possible transfer ...

The concept of the rough set was originally proposed by Pawlak as a formal tool for modelling and processing incomplete information in information systems, then in 1990, Dubois and Prade first introduced the rough fuzzy sets and fuzzy rough sets as a fuzzy extension of the rough sets. The aim of this paper is to present a new extension of the rough set theory by means of integrating the classic...

Covering-based rough sets is an extension of rough sets and it is based on a covering instead of a partition of the universe. Therefore it is more powerful in describing some practical problems than rough sets. However, by extending the rough sets, covering-based rough sets can increase the roughness of each model in recognizing objects. How to obtain better approximations from the models of a ...

Rough set theory has a significant importance in many fields. For example, some branches of artificial intelligence, such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, classification theory, cluster analysis, measurement theory and taxonomy. Also, in the domains of Medicine, Pharmacology, Banking, Market research and Engineering the rough set theory...

We show how definite extended logic programs can be used for defining and reason with rough sets. Moreover, a rough-set-specific query language is presented and an answering algorithm is outlined. Thus, we not only show a possible application of a paraconsistent logic to the field of rough sets as we also establish a link between rough set theory and logic programming, making possible transfer ...

The main objective of this special issue is to present readers with the significantly extended and improved versions of the articles presented at the International Conference on Rough Sets, Fuzzy Sets, and Granular Computing (RSFDGrC’05) held in Regina, Canada in September 2005. In the standard rough set model, the lower and upper approximations are defined based on the two extreme cases (full ...

This paper examines two interval based uncertain reasoning methods, one is based on interval fuzzy sets, and the other is based on rough sets. The notion of interval triangular norms is introduced. Basic issues on the use of t-norms for approximate reasoning with interval fuzzy sets are addressed. Inference rules are given for using both numeric intervals and lattice based intervals. The theory...

Pawlak’s Rough set theory was originally proposed as a general mathematical tool for dealing with uncertainty in modeling imperfect knowledge. The purpose of this paper is to introduce the concept of multifuzzy rough sets by combining the multi-fuzzy set and rough set models. Some operations such as Complement, Union, Intersection etc. are defined for multi-fuzzy rough sets and De Morgan’s laws...

Kidney failure remains one of the dreaded diseases worldwide. The factors of kidney problem/failure remain to be determined. The paper gives the basic idea of rough set theory. The procedure to carry out the operation of rough set theory with lower and upper approximation sets briefly explained. Some applications of rough set theory in the field of medicine w.r.t kidney failures are initiated a...

Rough sets, developed by Pawlak, are an important model of incomplete or partially known information. In this article, which is essentially a continuation of [11], we characterize rough sets in terms of topological closure and interior, as the approximations have the properties of the Kuratowski operators. We decided to merge topological spaces with tolerance approximation spaces. As a testbed ...