Cost sensitivity is an important problem, which has been addressed by many researchers
around the world. As far as cost sensitivity in the rough set theory is concerned, two types of …

Cost is an important issue in real world data mining. In rough set community, test cost and
decision cost are two popular costs which are addressed by many researchers. In recent …

For most attribute reduction in Pawlak rough set model (PRS), monotonicity is a basic
property for the quantitative measure of an attribute set. Based on the monotonicity, a series …

In the Pawlak rough set model, the positive region, the boundary region and the non-
negative region are monotonic with respect to the set inclusion of attributes. However, the …

In classical rough set models, attribute reduction generally keeps the positive or non-
negative regions unchanged, as these regions do not decrease with the addition of …

In this paper, we investigate enhancements to an upper classifier-a decision algorithm
generated by an upper classification method, which is one of the clsssification methods in …

Attribute reduction is one of the most important research issues in the rough set theory. The
purpose of attribute reduction is to find a minimal attribute subset that satisfies some specific …

Rough set is a tool with a mathematical foundation to deal with imprecise and imperfect
knowledge. It has been widely applied in machine learning, data mining and knowledge …

Attribute selection for rough sets is an NP-hard problem, in which fast heuristic algorithms
are needed to find reducts. In this project, two reduct methods for rough set were …

ABSTRACT The Rough Set (RS) theory can be considered as a tool to reduce the input
dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there …