The concept of attribute reducts plays a fundamental role in rough set analysis. There are at
least two possibilities to define an attribute reduct. A classification-based or global attribute …

Kernel methods and rough sets are two general pursuits in the domain of machine learning
and intelligent systems. Kernel methods map data into a higher dimensional feature space …

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 …

Urban industrial transformation is crucial for resource-based cities to break through the lock-
in effect of natural resources dependence and achieve economic, environmental and energy …

Feature subset selection presents a common challenge for the applications where data with
tens or hundreds of features are available. Existing feature selection algorithms are mainly …

As a soft computing tool, rough set theory has become a popular mathematical framework
for pattern recognition, data mining and knowledge discovery. It can only deal with attributes …

Multi-scale decision system (MDS) is an effective tool to describe hierarchical data in
machine learning. Optimal scale combination (OSC) selection and attribute reduction are …

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 …

Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Measures of relevance between features play an important role in classification and
regression analysis. Mutual information has been proved an effective measure for decision …