Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

Feature selection aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

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 …

Attribute reduction is one of the biggest challenges encountered in computational
intelligence, data mining, pattern recognition, and machine learning. Effective in feature …

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

Rough set theory, as one of the most useful soft computing methods dealing with vague and
uncertain information, has been successfully applied to many fields, and one of its main …

An information system is an important model in the field of artificial intelligence and its
information structures mean a mathematical structure of the family of information granules …

Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common
rough set theoretical models. Although the PRS can use equivalence classes to represent …

The selection of prominent features for building more compact and efficient models is an
important data preprocessing task in the field of data mining. The rough hypercuboid …

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 …