Fuzzy rough set theory can model uncertainty in data and has been applied to feature
selection for machine learning tasks. The existence of noise in data is one of the reasons for …

Fuzzy rough set theory can tackle feature redundancy in data and select more informative
features for machine learning tasks. Gaussian kernel is often coupled with fuzzy rough set …

Feature selection has aroused extensive attention and aims at selecting features that are
highly relevant to classification from raw datasets to improve the performance of a learning …

Fuzzy rough sets (FRSs) are considered to be a powerful model for analyzing uncertainty in
data. This model encapsulates two types of uncertainty: 1) fuzziness coming from the …

In order to solve the problem that the classical fuzzy rough set (FRS) model used for feature
selection is sensitive to noisy information, we propose an effective robust fuzzy rough set …

The existing fuzzy rough set (FRS) models all believe that the decision attribute divides the
sample set into several “clear” decision classes, and this data processing method makes the …

Feature selection has gained significant attention, with a focus on removing redundant or
irrelevant features to improve subsequent machine learning tasks. The fuzzy rough set is …

Fuzzy rough set (FRS) theory is generally used to measure the uncertainty of data. However,
this theory cannot work well when the class density of a data distribution differs greatly. In …

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 …

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 …