The concept of dependency in a neighborhood rough set model is an important evaluation
function for the feature selection. This function considers only the classification information …

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 …

Feature subset selection is viewed as an important preprocessing step for pattern
recognition, machine learning and data mining. Most of researches are focused on dealing …

Feature selection based on the fuzzy neighborhood rough set model (FNRS) is highly
popular in data mining. However, the dependent function of FNRS only considers the …

Feature selection in incomplete decision table has gained considerable attention in recently.
However many feature selection methods are mainly designed for incomplete data with …

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 …

Practical machine learning algorithms are known to degrade in performance (prediction
accuracy) when faced with many features (sometimes attribute is used instead of feature) …

Neighborhood rough set is a useful mathematic tool to describe uncertainty in mixed data.
Feature selection based on neighborhood rough set has been studied widely. However …

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 …

Feature subset selection presents a common challenge for the applications where data with
tens or hundreds of features are available. Existing feature selection algorithms are mainly …