Attribute reduction techniques based on Pawlak rough set theory work only on data sets with
discrete attributes. In real-world applications, the domain of a few or all attributes of the data …

Machine learning can extract desired knowledge and ease the development bottleneck in
building expert systems. Among the proposed approaches, deriving rules from training …

Attribute reduction is one of the core problems in Rough Set (RS) theory. In the Variable
Precision Rough Set (VPRS) model, attribute reduction faces two difficulties: firstly, in the …

Rough set theory, proposed in 1982 by Zdzislaw Pawlak, is in constant development. It is
concerned with the classification and analysis of imprecise or uncertain information and …

Rough set theory, as one of the most useful soft computing methods dealing with vague and
uncertain information, has been successfully applied to many fields, and one of its main …

Rough set theory is a new mathematic tool aimed at data analysis problems involving
uncertain or imprecise information. As an important extended rough set model, variable …

Rough sets theory has opened new trends for the development of the incomplete
information theory. Inside this one, the notion of reduct is a very significant one, but to obtain …

Significant indicators for measuring algorithms for attribute reduction include their fault
tolerance. The majority of existing algorithms rely on the incorporation of fault tolerance at …

Rough set theory is one of the effective methods to feature selection which can preserve the
characteristics of the original features by deleting redundant information. The main idea of …