We examine double successive approximations on a set, which we denote by L2L1, U2U1, U2L1, L2U1 where L1, U1 and L2, U2 are based on generally non-equivalent equivalence relations E1 and E2 respectively, on a finite non-empty set V. We consider the case of these operators being given fully defined on its powerset P(V ). Then, we investigate if we can reconstruct the equivalence relations which ...

This tutorial is a survey on rough set theory and some of its applications in Knowledge Discovery from Databases (KDD). It will also cover the practice guide to analysis of different real life problems using rough set methods as well as the presentation of Rough Set Exploration System (RSES) what can be treated as a preliminary material for the main conference and associated workshops.

In this article, a new hybrid model named linear Diophantine fuzzy rough set (LDFRS) is proposed to magnify the notion of (RS) and (LDFS). Concerning LDFRS, it more efficient discuss fuzziness roughness in terms approximation spaces (LDFA spaces); plays vital role information analysis, data computational intelligence. The concept (<p,p?>,<q,q?>)-indiscernibility relation (LDF relati...

High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough se...

A general ized model of rough sets called variable precision model (VP-model), a imed at modell ing classification problems involving uncertain or imprecise information, is presented. The general ized model inherits all basic mathematical propert ies of the original model introduced by Pawlak. The main concepts are introduced formally and illustrated with simple examples. The application of the...

This article gives an overview of the Rough Set Exploration System (RSES). RSES is a freely available software system toolset for data exploration, classification support and knowledge discovery. The main functionalities of this software system are presented along with a brief explanation of the algorithmic methods used by RSES. Many of the RSES methods have originated from rough set theory int...

Rough set is mainly concerned with the approximations of objects through an equivalence relation on a universe. Matroid is a combinatorial generalization of linear independence in vector spaces. In this paper, we define a parametric set family, with any subset of a universe as its parameter, to connect rough sets and matroids. On the one hand, for a universe and an equivalence relation on the u...

Decision-theoretic rough set models are a probabilistic extension of the algebraic rough set model. The required parameters for defining probabilistic lower and upper approximations are calculated based on more familiar notions of costs (risks) through the well-known Bayesian decision procedure. We review and revisit the decision-theoretic models and present new results. It is shown that we nee...

Rough set methodology is based on concept (set) approximations constructed from available background knowledge represented in information systems 14]. In many applications only partial knowledge about approximated concepts is given. Hence quite often rst a parametrized family of concept approximations is built and next by tuning of the parameters the best, in a sense, approximation is chosen (s...

This paper proposes an approach to training rough set models using Bayesian framework trained using Markov Chain Monte Carlo (MCMC) method. The prior probabilities are constructed from the prior knowledge that good rough set models have fewer rules. Markov Chain Monte Carlo sampling is conducted through sampling in the rough set granule space and Metropolis algorithm is used as an acceptance cr...