Due to the explosive growth of electronically stored information, automatic methods must be
developed to aid users in maintaining and using this abundance of information effectively. In …

Attribute selection (AS) refers to the problem of selecting those input attributes or features
that are most predictive of a given outcome; a problem encountered in many areas such as …

Feature selection (FS) refers to the problem of selecting those input attributes that are most
predictive of a given outcome; a problem encountered in many areas such as machine …

Feature selection refers to the problem of selecting those input features that are most
predictive of a given outcome; a problem encountered in many areas such as machine …

Semantics-preserving dimensionality reduction refers to the problem of selecting those input
features that are most predictive of a given outcome; a problem encountered in many areas …

Attribute reduction is one of the biggest challenges encountered in computational
intelligence, data mining, pattern recognition, and machine learning. Effective in feature …

Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …

Each year worldwide, more and more data is collected. In fact, it is estimated that the amount
of data collected and stored at least doubles every 2years. Of this data, a large percentage is …

Feature subset selection has become an important challenge in areas of pattern recognition,
machine learning and data mining. As different semantics are hidden in numerical and …

Classical rough set theory is considered a useful tool for dealing with the uncertainty of
categorical data. The major deficiency of this model is that the classical rough set model is …