Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

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 …

The key strategy of the three-way decisions theory is to consider a decision-making problem
as a ternary classification one (ie acceptance, rejection and non-commitment). Recently, this …

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 …

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 …

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 …

At present, there is no unified method for solving multiattribute decision-making problems. In
this paper, we propose two methods that benefit from some novel fuzzy rough set models …