Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …

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 …

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 …

For heterogeneous data sets containing numerical and symbolic feature values, feature
selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very …

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 …

Classical rough set theory is considered a useful tool for dealing with the uncertainty of
categorical data. The major deficiency of this model is that the classical rough set model is …

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 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 …

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 …

Rough set theory has been extensively discussed in machine learning and pattern
recognition. It provides us another important theoretical tool for feature selection. In this …