Affiliations: [a] Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium. [email protected] | [b] Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium | [c] Inflammation Research Center, Flemish Institute for Biotechnology, Belgium; Department of Respiratory Medicine, Ghent University, Belgium | [d] Department of Comp. Sci. and Artificial Intelligence, University of Granada, Spain; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium
Correspondence:
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Address for correspondence: Department of Applied Mathematics, Computer Science and Statistics Ghent University, Belgium
Note: [†] Also works: Inflammation Research Center, Flemish Institute for Biotechnology, Belgium
Abstract: 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 of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications. We recall their integration in preprocessing methods and consider learning algorithms in the supervised, unsupervised and semi-supervised domains and outline future challenges. Throughout the paper, we highlight the interaction between theoretical advances on fuzzy rough sets and practical machine learning tools that take advantage of them.
