Rough set theory is a relatively new mathematical tool for computer applications in circumstances characterized by vagueness and uncertainty. In this paper, we address uncertainty of rough sets for incomplete information systems. An axiom definition of knowledge granulation for incomplete information systems is obtained, under which a measure of uncertainty of a rough set is proposed. This meas...

Contingency tables represent data in a granular way and are a well-established tool for inductive generalization of knowledge from data. We show that the basic concepts of rough sets, such as concept approximation, indiscernibility, and reduct can be expressed in the language of contingency tables. We further demonstrate the relevance to rough sets theory of additional probabilistic information...

This keynote talk considers how one might utilize fuzzy sets, near sets, and rough sets, taken separately or taken together in hybridizations in solving a variety of problems commonly faced in science and engineering. These technologies offer set theoretic approaches to solving problems such as classifying sensor output, image retrieval and image correspondence. Fuzzy sets result from the intro...

We investigate the operators associated with approximations in the rough set theory introduced by Pawlak in his [Paw82,MP84] and extensively studied by the Rough Set community [RS06]. We use universal algebra techniques to establish a natural characterization of operators associated with rough sets.

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 ...

The decision-theoretic rough set model based on Bayesian decision theory is a main development tendency in the research of rough sets. To extend the theory of decision-theoretic rough set, the article devotes this study to presenting multigranulation decision-theoretic rough set model in ordered information systems. This new multigranulation decision-theoretic rough set approach is characterize...

This paper highlights the study of two classification methods, Rough Sets Theory (RST) and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two classification methods we can easily and accurately predict LD in any child. Also, we...

—Covering-based rough set theory is an extension to classical rough set. The main purpose of this paper is to study covering rough sets from a topological point of view. The relationship among upper approximations based on topological spaces are explored.