There has been great interest in developing methodologies that are capable of dealing with
imprecision and uncertainty. The large amount of research currently being carried out in …

Attribute selection (AS) refers to the problem of selecting those input attributes or features
that are most predictive of a given outcome; a problem encountered in many areas such as …

Feature selection (FS) refers to the problem of selecting those input attributes that are most
predictive of a given outcome; a problem encountered in many areas such as machine …

Feature selection based on fuzzy rough sets is an effective approach to select a compact
feature subset that optimally predicts a given decision label. Despite being studied …

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 …

Fuzzy rough set method provides an effective approach to data mining and knowledge
discovery from hybrid data including categorical values and numerical values. However, its …

Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

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 …

The fuzzy set theory and the rough set theory are two distinct but complementary theories
that deal with uncertainty in data. The salient features of both the theories are encompassed …

Classical fuzzy rough set often uses fuzzy rough dependency as an evaluation function of
feature selection. However, this function only retains the maximum membership degree of a …