摘要在阐释粗糙集理论基本体系结构的基础上, 从多个角度探讨粗糙集模型的研究思路,
分析粗糙集理论与模糊集, 证据理论, 粒计算, 形式概念分析, 知识空间等其它理论之间的联系 …

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 …

Rough set theory can be applied to rule induction. There are two different types of
classification rules, positive and boundary rules, leading to different decisions and …

Feature selection is an important task in machine learning, which can effectively reduce the
dataset dimensionality by removing irrelevant and/or redundant features. Although a large …

The original Rough Set model is concerned primarily with algebraic properties of
approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the …

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 …

In rough set theory, a decision class (ie, a subset of objects) is approximated by three pair-
wise disjoint positive, boundary, and negative regions. The concept of three-way decisions …

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 …

The concept of probabilistic rough sets, as a main subject of this paper, is intimately
connected with the concept of decision-theoretic rough sets. This paper investigates fuzzy …

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 …