Neighborhood rough set model is considered as one of the effective granular computing
models in dealing with numerical data. This model is now widely discussed in feature …

Granular computing aims to develop a granular view for interpreting and solving problems.
The model of neighborhood rough sets is one of effective tools for granular computing. This …

In real applications of data mining, machine learning and granular computing, measurement
errors, test costs and misclassification costs often occur. Furthermore, the test cost of a …

Feature selection is an important preprocessing step in data mining and pattern recognition.
The neighborhood rough set (NRS) model is a widely-used rough set model for 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 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 …

With increases in feature dimensions and the emergence of hierarchical class structures,
hierarchical feature selection has become an important data preprocessing step in machine …

Exploring feature significance associated with label is a fundamental task in the architecture
of feature selection. Nevertheless, most of the existing schemes are limited by the global …

Fuzzy rough set theory can model uncertainty in data and has been applied to feature
selection for machine learning tasks. The existence of noise in data is one of the reasons for …

Feature selection based on neighborhood rough sets (NRSs) has become a popular area of
research in data mining. However, the limitation that NRSs inherently ignore the differences …