In this paper, a variable-precision dominance-based rough set approach (VP-DRSA) is proposed together with several VP-DRSA-based approaches to attribute reduction. The properties of VP-DRSA are shown in comparison to previous dominance-based rough set approaches. An advantage of VP-DRSA over variable-consistency dominance-based rough set approach in decision rule induction is emphasized. Some r...

In this paper we extend the notion of rough convergence using the concept of ideals which automatically extends the earlier notions of rough convergence and rough statistical convergence. We define the set of rough ideal limit points and prove several results associated with this set.

In this paper, we study soft rough BCI-algebras with respect to MSapproximation spaces. Some new soft rough operations over BCI-algebras are explored. In particular, lower and upper soft rough BCI-algebras with another soft set are investigated. Finally, a kind of decision making method for soft rough BCI-algebras are originally investigated.

In this paper, we discuss rough inclusions defined in Rough Mereology – a paradigm for approximate reasoning introduced by Polkowski and Skowron – as a basis for common models for rough as well as fuzzy set theories. We justify the point of view that tolerance (or, similarity) is the motif common to both theories. To this end, we demonstrate in Sect. 6 that rough inclusions (which represent a h...

The objective of this chapter is to provide a semantic framework for fuzzy sets in the theory of rough sets. Rough membership functions are viewed as a special type of fuzzy membership functions interpretable using conditional probabilities. The relationships between fuzzy membership functions and rough membership functions, between core and support of fuzzy set theory and lower and upper appro...

This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attribute...

As it is well known the rough set a beneficial method for data uncertainty analysis. However, this time- consuming task many big sets. So we utilized concept of local sets in analysis children addicted to social media handle efficiently and give some properties. With results, proved that gave more concrete clear information than

0198-9715/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compenvurbsys.2013.09.005 ⇑ Corresponding author. Address: Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box 14115-111, Tehran, Iran. Tel.: +98 21 82884698; fax: +98 21 82884180. E-mail addresses: [email protected] (J. Karami), [email protected] (A. Alimohammadi), Taseifouri@y...

In this paper, we would like to present some modal logics with semantics based on rough set theory and related notions. In addition to surveying some well-known results about the links between modal logics and rough set theory, we also develop some new applied logics inspired I)y generalized rough set theory. Kcywor(ls: Rough set, modal logic, cpistcmic logic. 1 I n t r o d u c t i o n Tile rou...