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 …

Attribute reduction with rough sets aims to delete superfluous condition attributes from a
decision system by considering the inconsistency between condition attributes and the …

Attribute reduction is one of the most important applications of fuzzy rough sets in machine
learning and pattern recognition. Most existing methods employ the intersection operation of …

The k-nearest-neighbor rule is a popular classification technique, and rough set theory is an
effective mathematical tool to deal with the uncertainty of data. Rough set models based on k …

Fuzzy rough sets are considered as an effective tool to deal with uncertainty in data analysis,
and fuzzy similarity relations are used in fuzzy rough sets to calculate similarity between …

Attribute reduction with variable precision rough sets (VPRS) attempts to select the most
information-rich attributes from a dataset by incorporating a controlled degree of …

Neighborhood rough sets based attribute reduction, as a common dimension reduction
method, has been widely used in machine learning and data mining. Each attribute has the …

Attribute reduction is one of the important applications in fuzzy rough set theory. However,
most attribute reduction methods in fuzzy rough theory mainly focus on removing irrelevant …

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 …

Rough set theory is an essential tool for measuring uncertainty, which has been widely
applied in attribute reduction algorithms. Most of the related researches focus on how to …