In the field of rough set, how to improve the efficiency of obtaining reduct has been paid
much attention to. One of the typical strategies is to reduce the number of comparisons …

Presently, attribute reduction, as one of the most important topics in the field of rough set,
has been widely explored from different perspectives. To derive the qualified reduct defined …

By considering the information granulation in Granular Computing, the concept of the multi-
granularity is important. It is mainly because different results of information granulation will …

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 …

In this paper, we propose an efficient quick attribute reduct algorithm based on
neighborhood rough set model. In this algorithm we divide the objects (records) of the whole …

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 …

Attribute reduction plays an important role in the areas of rough sets and granular
computing. Many kinds of attribute reducts have been defined in previous studies. However …

Attribute reduction is an important research concept in rough set theory. Many attribute
reduction algorithms were designed for the static information system in the past years …

In the fields of rough set and machine learning, attribute reduction has been demonstrated to
be effective in removing redundant attributes with clear explanations. Therefore, not only the …

Attribute reduction is one of the most important research issues in the rough set theory. The
purpose of attribute reduction is to find a minimal attribute subset that satisfies some specific …