Despite the importance of decision-making (DM) techniques for construction of effective
decision models for supplier selection, there is a lack of a systematic literature review for it …

The advent of Web 2.0 and its continuous growth has yielded enormous amounts of freely
available user-generated information. Within this information, it is easy to find subjective …

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 …

Feature selection in the data with different types of feature values, ie, the heterogeneous or
mixed data, is especially of practical importance because such types of data sets widely …

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 …

Feature selection has aroused considerable research interests during the last few decades.
Traditional learning-based feature selection methods separate embedding learning and …

In motor imagery-based Brain Computer Interfaces (BCI), discriminative patterns can be
extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) …

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

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