Probabilistic approaches have been applied to the theory of rough set in several forms,
including decision-theoretic analysis, variable precision analysis, and information-theoretic …

The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …

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 …

Data reduction plays an important role in machine learning and pattern recognition with a
high-dimensional data. In real-world applications data usually exists with hybrid formats, and …

Rough set theory has proven to be an efficient tool for modeling and reasoning with
uncertainty information. By introducing probability into fuzzy approximation space, a theory …

This paper presents a review of the current literature on rough-set-and near-set-based
approaches to solving various problems in medical imaging such as medical image …

Feature selection (FS) refers to the problem of selecting those input attributes that are most
predictive of a given outcome; a problem encountered in many areas such as machine …

With the rapid development of the Chinese economy in the last thirty years, crude oil has
become an essential commodity and a strategic resource for China. Thus, the topic of crude …

Traditional rough set theory uses equivalence relations to compute lower and upper
approximations of sets. The corresponding equivalence classes either coincide or are …