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 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 …

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 …

Feature Selection (FS) or Attribute Reduction techniques are employed for dimensionality
reduction and aim to select a subset of the original features of a data set which are rich in the …

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 …

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 …

One of the main obstacles facing current fuzzy modelling techniques is that of dataset
dimensionality. To enable these techniques to be effective, a redundancy-removing step is …

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 …

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