Feature selection is a meaningful aspect of data mining that aims to select more relevant
data features and provide more concise and explicit data descriptions. It is beneficial for …

Failure mode and effect analysis (FMEA) is a proactive quality management instrument to
improve the reliability of systems. Nevertheless, the classical FMEA technique has suffered …

Attribute reduction is one of the important applications in fuzzy rough set theory. However,
most attribute reduction methods in fuzzy rough theory mainly focus on removing irrelevant …

In the fields of rough set and machine learning, attribute reduction has been demonstrated to
be effective in removing redundant attributes with clear explanations. Therefore, not only the …

The fuzzy rough set theory has been widely used to deal with uncertainty in real-valued or
even complex data, in which one of the most concerned issues is feature selection. Since a …

The reduction of the set of attributes is an important preliminary challenge in order to obtain
information from knowledge systems. Two remarkable formal tools for extracting such …

Three-way decision is widely applied with rough set theory to learn classification or decision
rules. The approaches dealing with complete information are well established in the …

Due to technological advancement, data size has seen a significant increase both in terms
of features and instances. An efficient way to handle large sized datasets is to apply data …

Ensemble learning is an effective approach for improving the generalization ability of base
classifiers. To generate a set of accurate and diverse base classifiers, different data …

There exist two formulations of the theory of rough sets, consisting of the conceptual
formulations and the computational formulations. Class-specific and classification-based …