Feature selection plays a vital role in many areas of pattern recognition and data mining.
The effective computation of feature selection is important for improving the classification …

Feature selection (attribute reduction) from large-scale incomplete data is a challenging
problem in areas such as pattern recognition, machine learning and data mining. In rough …

Feature selection is a challenging problem in many areas such as pattern recognition,
machine learning and data mining. Rough set theory, as a valid soft computing tool to …

In many fields, such as data mining, machine learning and pattern recognition, datasets
containing large numbers of features are often involved. In such cases, feature selection is …

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 with rough sets aims to delete redundant conditional features from static
data by considering single type features. However, traditional feature selection methods …

Feature selection plays an important role in pattern recognition and machine learning.
Confronted with high dimensional data in many data analysis tasks, feature selection …

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 …

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 …

Incremental feature selection can improve learning of accumulated data. We focus on
incremental feature selection based on rough sets, which along with their generalizations …