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 …

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 …

The neighborhood rough set theory is a helpful instrument for working with data that is
numerical, and the performance of its uncertainty measures is generally stable. Even one …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

Existing feature selection methods easily neglect the distribution of data, and require most of
the neighborhood radius in neighborhood rough sets (NRS) to be selected artificially. These …

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 aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

Rough set approach is one of effective feature selection methods that can preserve the
meaning of the features. The essence of feature selection based on rough set approach is to …

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 …

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 …