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 …

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 …

Feature selection is a challenging problem in areas such as pattern recognition, machine
learning and data mining. Considering a consistency measure introduced in rough set …

Feature subset selection is viewed as an important preprocessing step for pattern
recognition, machine learning and data mining. Most of researches are focused on dealing …

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

Tumor classification based on gene expression levels is important for tumor diagnosis.
Since tumor data in gene expression contain thousands of attributes, attribute selection for …

Data reduction plays an important role in machine learning and pattern recognition with a
high-dimensional data. In real-world applications data usually exists with hybrid formats, and …

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 …

A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of
decision system and it has been applied successfully in all the fields. It is used to identify the …

The rough and fuzzy set approaches presented here open up many new frontiers for
continued research and development Computational Intelligence and Feature Selection …