Worldwide, there has been a rapid growth in interest in rough set theory and its applications
in recent years. Evidence of this can be found in the increasing number of high-quality …

The volume of data on the social media is huge and even keeps increasing. The need for
efficient processing of this extensive information resulted in increasing research interest in …

Feature selection is a challenging problem in areas such as pattern recognition, machine
learning and data mining. Considering a consistency measure introduced in rough set …

Rough set approach to knowledge-based decision support Page 1 ELSEVIER European
Joumal of Operational Research 99 (1997) 48-57 EUROPEAN JOURNAL OF …

We present applications of rough set methods for feature selection in pattern recognition. We
emphasize the role of the basic constructs of rough set approach in feature selection …

Tumor classification based on gene expression levels is important for tumor diagnosis.
Since tumor data in gene expression contain thousands of attributes, attribute selection for …

A new method of computing all optimal certain rules from an incomplete information system
is presented and proved. The method does not require changing the size of the original …

The present article provides a survey of the available literature on data mining using soft
computing. A categorization has been provided based on the different soft computing tools …

A new version of the rule induction system LERS is described and compared with the old
version of LERS. Experiments were done for comparison of performance for both versions of …

As a supervised learning method, classical rough set theory often requires a large amount of
labeled data, in which concept approximation and attribute reduction are two key issues …