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Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods

Sarah Vluymans (UGent)
(2018)
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Chicago
Vluymans, Sarah. 2018. “Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods”. Ghent, Belgium ; Granada, Spain: Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence.
APA
Vluymans, S. (2018). Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods. Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence, Ghent, Belgium ; Granada, Spain.
Vancouver
1.
Vluymans S. Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods. [Ghent, Belgium ; Granada, Spain]: Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence; 2018.
MLA
Vluymans, Sarah. “Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods.” 2018 : n. pag. Print.
@phdthesis{8567516,
  author       = {Vluymans, Sarah},
  language     = {eng},
  pages        = {XII, 248},
  publisher    = {Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence},
  school       = {Ghent University},
  title        = {Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods},
  year         = {2018},
}