“Our claim is that knowledge is deep–seated in the classificatory abilities of human beings and other species. For example, knowledge about the environment is primarily manifested as an ability to classify a variety of situations from the point of view of survival in the real world : : : Classification on more abstract levels, seems to be a key issue in reasoning, learning and decision making, ...

In this paper, we propose a new rough set classifier induced from partially uncertain decision system. The proposed classifier aims at simplifying the uncertain decision system and generating more significant belief decision rules for classification process. The uncertainty is reperesented by the belief functions and exists only in the decision attribute and not in condition attribute values.

I n t,his pappr, t,hr principle and experiment a1 resu1t.s of an atI.ritiut.e-orieiit.pd rough set. approach for knowledge tiisc,overy in databases are described. Our riiet.hod integriLt(Js thc &abase operation, rough set t 'hrory and ma( .hi l i (> lcarnirig t ,echiques. In our rriethod, we consider t )J? learning procedure consisk of t.wo phases: dat,a genwiilization aiid data reduction. I r ...

Abs t rac t . Vagueness for a long time has been studied by philosophers, logicians and linguists. Recently researchers interested in AI contributed essentially to this area. In this paper we present a new approach to vagueness, called rough set theory. The starting of the theory theory is the assumption that fundamental mechanisms of human reasoning are based on the ability to classify object ...

A fuzzy set can be represented by a family of crisp sets using its α-level sets, whereas a rough set can be represented by three crisp sets. Based on such representations, this paper examines some fundamental issues involved in the combination of rough-set and fuzzy-set models. The rough-fuzzy-set and fuzzy-rough-set models are analyzed, with emphasis on their structures in terms of crisp sets....

Soft set theory is a newly emerging tool to deal with uncertain problems. Based on soft sets, soft rough approximation operators are introduced, and soft rough sets are defined by using soft rough approximation operators. Soft rough sets, which could provide a better approximation than rough sets do, can be seen as a generalized rough set model. This paper is devoted to investigating soft rough...

Description of the pairs 〈low approximation, upper approximation〉 of rough sets is an important aspect in the research of rough set theory by algebraic method. By defining some basic operators on the approximation pairs, rough algebras can be constructed. Then some general algebras can be selected to describe the pairs of rough sets. The most famous rough algebras are Rough Double Stone Algebra...

Rough set theory uses the concept of upper and lower approximations to encapsulate inherent inconsistency in real-world objects. Information multisystems are represented using multisets instead of crisp sets. This paper begins with an overview of recent works on multisets and rough sets. Rough multiset is introduced in terms of lower and upper approximations and explores related properties. The...