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 …

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 an important model in feature selection. However, it only
determines the granularity of the neighborhood from a feature perspective, while ignoring …

Rough sets have been widely used in the fields of machine learning and feature selection.
However, the classical rough sets have the problems of difficultly dealing with real-value …

Feature selection of the neighborhood rough set is an important step in preprocessing the
data and improving classification performance. Neighborhood granules form the basis for …

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 …

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 …

Dominance-based neighborhood rough set (DNRS) is capable to give qualitative and
quantitative descriptions of the relations between ordered objects. In spite of its effectiveness …

Rough set theory, as an academic hotspot in the field of artificial intelligence, has provided a
solid theoretical foundation for feature selection. However, with the continuous updating of …

Feature selection using variable precision neighborhood rough sets (VPNRS) has garnered
considerable attention in data mining and knowledge discovery. Nevertheless, the positive …