Feature selection, especially for large data sets, is a challenging problem in areas such as
pattern recognition, machine learning and data mining. With the development of data …

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 …

Significant indicators for measuring algorithms for attribute reduction include their fault
tolerance. The majority of existing algorithms rely on the incorporation of fault tolerance at …

This paper presents a strong data-mining method based on a rough set, which can
simultaneously realize feature selection, classification, and knowledge representation …

Dataset dimensionality is one of the primary impediments to data analysis in areas such as
pattern recognition, data mining, and decision support. A feature subset that possesses the …

Feature selection is viewed as the problem of finding the minimal number of features from an
original set with the minimum information loss. Due to its high importance in the fields of …

Feature selection facilitates pattern recognition, and fuzzy neighborhood rough sets provide
an effective tool. By fuzzy neighborhood rough sets, we propose a heuristic feature selection …

In order to solve the problem that the classical fuzzy rough set (FRS) model used for feature
selection is sensitive to noisy information, we propose an effective robust fuzzy rough set …

Fuzzy rough set method provides an effective approach to data mining and knowledge
discovery from hybrid data including categorical values and numerical values. However, its …

Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …