Neighborhood rough set is a powerful tool to deal with continuous value information
systems. Graphics processing unit (GPU) computing can efficiently accelerate the …

In information systems, there may exist multiple different types of attributes like categorical
attributes, numerical attributes, set-valued attributes, interval-valued attributes, missing …

Attribute reduction is a hot topic in rough set research. As an extension of rough sets,
neighborhood rough sets can effectively solve the problem of information loss after data …

Attribute reduction is an important preprocessing step in machine learning and pattern
recognition. This paper introduces condition-attribute Boolean matrix and decision-attribute …

Attribute reduction is a hot topic in the field of data mining. Compared with the traditional
methods, the attribute reduction algorithm based on covering rough set is more suitable for …

The variable precision rough set model resists noise in data by introducing a parameter to
relax the strict inclusion in approximations of the classical rough set model, and attribute …

In information systems, there may exist multiple different types of attributes like categorical
attributes, numerical attributes, set-valued attributes, interval-valued attributes, missing …

Attribute reduction is an important issue in rough set theory. Many efficient algorithms have
been proposed, however, few of them can process huge data sets quickly. In this paper …

Rough set theory is emerging as a powerful toll for reasoning about data, attribute reduction
is one of the important topics in the research on the rough set theory. At present, there are …

The neighborhood rough set model only focuses on the consistent samples whose
neighborhoods are completely contained in some decision classes, and ignores the …