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 …

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 …

Unsupervised attribute reduction becomes very challenging due to a lack of decision
information, which is to select a subset of attributes that can maintain learning ability without …

With the rapid development of multimedia technology, massive unlabelled data with high
dimensionality need to be processed. As a means of dimensionality reduction, unsupervised …

Aiming at the problem of effectively selecting relevant features from heterogeneous data
without decision, a novel feature selection approach is studied based on fuzzy mutual …

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 …

Features extracted from real world applications increase dramatically, while machine
learning methods decrease their performance given the previous scenario, and feature …

Fuzzy rough set theory has been proved to be an effective tool to deal with uncertainty data.
Some different forms of fuzzy uncertainty measures have been introduced in fuzzy rough set …

The theory of fuzzy rough sets is an effective soft computing paradigm for dealing with
vague, uncertain, or imprecise data. However, most existing fuzzy rough sets-based …

Data used in machine learning applications is prone to contain both vague and incomplete
information. Many authors have proposed to use fuzzy rough set theory in the development …