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Applications of fuzzy rough set theory in machine learning: a survey

Sarah Vluymans (UGent) , Lynn D'eer (UGent) , Yvan Saeys (UGent) and Chris Cornelis (UGent)
(2015) FUNDAMENTA INFORMATICAE. 142(1-4). p.53-86
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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.
Keywords
rough sets, fuzzy sets, fuzzy rough sets, machine learning

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Chicago
Vluymans, Sarah, Lynn D’eer, Yvan Saeys, and Chris Cornelis. 2015. “Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey.” Fundamenta Informaticae 142 (1-4): 53–86.
APA
Vluymans, S., D’eer, L., Saeys, Y., & Cornelis, C. (2015). Applications of fuzzy rough set theory in machine learning: a survey. FUNDAMENTA INFORMATICAE, 142(1-4), 53–86.
Vancouver
1.
Vluymans S, D’eer L, Saeys Y, Cornelis C. Applications of fuzzy rough set theory in machine learning: a survey. FUNDAMENTA INFORMATICAE. 2015;142(1-4):53–86.
MLA
Vluymans, Sarah, Lynn D’eer, Yvan Saeys, et al. “Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey.” FUNDAMENTA INFORMATICAE 142.1-4 (2015): 53–86. Print.
@article{7017049,
  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.},
  author       = {Vluymans, Sarah and D'eer, Lynn and Saeys, Yvan and Cornelis, Chris},
  issn         = {0169-2968},
  journal      = {FUNDAMENTA INFORMATICAE},
  language     = {eng},
  number       = {1-4},
  pages        = {53--86},
  title        = {Applications of fuzzy rough set theory in machine learning: a survey},
  url          = {http://dx.doi.org/10.3233/FI-2015-1284},
  volume       = {142},
  year         = {2015},
}

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