Feature selection acts as an important preprocessing method to reduce redundant
information. In order to effectively evaluate the classification information hidden in a given …

As a novel fuzzy covering, fuzzy covering has attracted considerable attention. However, the
traditional fuzzy--covering-based rough set and most of its extended models cannot well fit …

Selection of important attributes/features from decision information systems plays a vital role
in data mining and machine learning tasks. It is regarded as a very interesting, but challenge …

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy
rough dependency as a criterion for feature selection. However, this model can merely …

Rough set theory has been extensively discussed in machine learning and pattern
recognition. It provides us another important theoretical tool for feature selection. In this …

As a reliable and valid tool for analyzing uncertain information, fuzzy rough set theory has
attracted widespread concern in feature selection. However, the performance of fuzzy rough …

Rough sets, especially fuzzy-rough sets, have proven to be a powerful tool for dealing with
vagueness and uncertainty in data analysis. Fuzzy-rough feature selection has been shown …

At present, there is no unified method for solving multiattribute decision-making problems. In
this paper, we propose two methods that benefit from some novel fuzzy rough set models …

Attribute selection is one of the important problems encountered in pattern recognition,
machine learning, data mining, and bioinformatics. It refers to the problem of selecting those …

The comparable property (or inclusion property) is the basic property of rough set theory.
Unfortunately, the well-known granular variable precision fuzzy rough set does not satisfy …