Attribute reduction is one of the most fundamental and important topics in rough set theory.
Uncertainty measures play an important role in attribute reduction. In the classical rough set …

Decision-theoretic rough set model, as a probabilistic generalization of the Pawlak rough set
model, is an effective method for decision making from vague, uncertain or imprecise data …

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 …

Attribute reduction is an important application of rough set theory. Most existing rough set
models do not consider the weight information of attributes in information systems. In this …

Attribute reduction is considered an important preprocessing step in machine learning,
pattern recognition, and data mining, and several attribute reduction measures based on …

In this paper, attribute reduction in variable precision rough set model is discussed. Several
kinds of reducts preserving some of lower approximations, upper approximations, boundary …

Rough set theory is widely used to deal with uncertainty. Original rough set model is mainly
based on equivalence relations. To extend the application scope, classical rough set model …

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 …

There exist two formulations of the theory of rough sets, consisting of the conceptual
formulations and the computational formulations. Class-specific and classification-based …

Bayesian rough set model (BRSM), as the hybrid development between rough set theory
and Bayesian reasoning, can deal with many practical problems which could not be …