Feature selection (FS), as one of the most significant preprocessing techniques in the fields
of machine learning and pattern recognition, has received great attention. In recent years …

Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …

Abstract Knowledge discovery for data streaming requires online feature selection to reduce
the complexity of real-world datasets and significantly improve the learning process. This is …

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 …

Fuzzy rough set theory is sensitive to noisy samples as the fuzzy approximations are
proposed based on sensitive statistics, ie minimum and maximum. Here, we develop a …

Fuzzy and intuitionistic fuzzy β covering has attracted the interest of many researchers
recently. However, some of the factors namely 1. the lack of inclusion relationship between …

Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …

To boost the performance level of big data, data partitioning is considered to be as the
backbone of big data applications. In recent years, many researchers are focusing their work …

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 …

As one of the most relevant data preprocessing techniques, discretization has played an
important role in data mining, which is widely applied in industrial control. It can transform …