This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

The fuzzy rough set is one of the most effective methods for dealing with the fuzziness and
uncertainty of data. However, in most cases this model only considers the information …

Attribute reduction is one of the most important applications of fuzzy rough sets in machine
learning and pattern recognition. Most existing methods employ the intersection operation of …

The volume of data on the social media is huge and even keeps increasing. The need for
efficient processing of this extensive information resulted in increasing research interest 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 …

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 …

In this study, a granular ball based selector was developed for reducing the dimensions of
data from the perspective of attribute reduction. The granular ball theory offers a data …

As a typical multigranularity data analysis model, multi-scale rough sets have attracted
considerable attention in recent years. However, classical multi-scale rough sets and most of …

Attribute reduction is viewed as an important preprocessing step for pattern recognition and
data mining. Most of researches are focused on attribute reduction by using rough sets …

Feature selection is an important preprocessing step in machine learning and data mining,
and feature criterion arises a key issue in the construction of feature selection algorithms …