Applications of Fuzzy Rough Set Theory in Machine Learning : a Survey

• Autorzy: Vluymans S. , D’eer L. , Saeys Y. , Cornelis C.

Identyfikatory

DOI

10.3233/FI-2015-1284

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

fuzzy sets; rough sets; fuzzy rough sets; machine learning

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2015
• Tom: Vol. 142, nr 1-4
• Strony: 53--86
• Opis fizyczny: Bibliogr. 165 poz., rys.

Twórcy

autor

Vluymans S.

• Department of Applied Mathematics Computer Science and Statistics Ghent University, Belgium

autor

D’eer L.

• Department of Applied Mathematics Computer Science and Statistics Ghent University, Belgium

autor

Saeys Y.

• Inflammation Research Center Flemish Institute for Biotechnology, Belgium Department of Respiratory Medicine Ghent University, Belgium

autor

Cornelis C.

• Department of Comp. Sci. and Artificial Intelligence University of Granada, Spain Department of Applied Mathematics Computer Science and Statistics Ghent University, Belgium

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