Attribute reduction, also known as feature selection, is a common problem by selecting a
subset of relevant attributes (eg features) to reach efficient learning/mining. Many attribute …

As data dimensions and volume rapidly increase, attribute reduction using the original data
becomes computationally infeasible. Large data frequently contain various redundant …

Attribute reduction has shown its effectiveness in improving the performance of classifiers.
Different from widely studied supervised attribute reduction, unsupervised attribute reduction …

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 plays an important role in pattern recognition and machine learning, and
the theory of rough sets has become a commonly used model for attribute reduction since its …

Optimal reduct is one of the challenging problems in rough set theory, and most of the
existing algorithms cannot achieve the optimal reduct on high dimensional data sets. To …

Attribute reduction is an important application of rough sets. Efficiently reducing massive
dynamic data sets quickly has always been a major goal of researchers. Traditional …

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 …

Attribute reduction is a key problem in many areas such as data mining, pattern recognition,
machine learning. The problems of finding all reducts as well as finding a minimal reduct in …

In current supervised machine learning research spectrum, there are several attribute
reduction methodologies to acquire reducts with low test cost. They can deal with symbolic …