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 …

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

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 …

Conventional k-nearest neighbor (KNN) classification approaches have several limitations
when dealing with some problems caused by the special datasets, such as the sparse …

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

Set-valued information systems are generalized models of single-valued information
systems. In this paper, we propose two new relations for set-valued information systems …

Uncertainty measures can supply new viewpoints for analyzing data. They can help us in
disclosing the substantive characteristics of data. The uncertainty measurement issue is also …

This paper proposes a modification to the analytic hierarchy process (AHP) to select the
most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) …

With rapidly increasing data, clustering algorithms are important tools for data analytics in
modern research. They have been successfully applied to a wide range of domains, for …