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Multi-label classification using a fuzzy rough neighborhood consensus

Sarah Vluymans (UGent) , Chris Cornelis (UGent) , Francisco Herrera and Yvan Saeys (UGent)
(2018) INFORMATION SCIENCES. 433-434. p.96-114
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Keywords
Fuzzy rough set theory, Multi-label classification, Nearest neighbor classification, SETS, CLASSIFIERS, PREDICTION, ALGORITHM, OPERATORS, RANKING, MODELS

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Citation

Please use this url to cite or link to this publication:

Chicago
Vluymans, Sarah, Chris Cornelis, Francisco Herrera, and Yvan Saeys. 2018. “Multi-label Classification Using a Fuzzy Rough Neighborhood Consensus.” Information Sciences 433-434: 96–114.
APA
Vluymans, S., Cornelis, C., Herrera, F., & Saeys, Y. (2018). Multi-label classification using a fuzzy rough neighborhood consensus. INFORMATION SCIENCES, 433-434, 96–114.
Vancouver
1.
Vluymans S, Cornelis C, Herrera F, Saeys Y. Multi-label classification using a fuzzy rough neighborhood consensus. INFORMATION SCIENCES. 2018;433-434:96–114.
MLA
Vluymans, Sarah, Chris Cornelis, Francisco Herrera, et al. “Multi-label Classification Using a Fuzzy Rough Neighborhood Consensus.” INFORMATION SCIENCES 433-434 (2018): 96–114. Print.
@article{8544061,
  author       = {Vluymans, Sarah and Cornelis, Chris and Herrera, Francisco and Saeys, Yvan},
  issn         = {0020-0255},
  journal      = {INFORMATION SCIENCES},
  keywords     = {Fuzzy rough set theory,Multi-label classification,Nearest neighbor classification,SETS,CLASSIFIERS,PREDICTION,ALGORITHM,OPERATORS,RANKING,MODELS},
  language     = {eng},
  pages        = {96--114},
  title        = {Multi-label classification using a fuzzy rough neighborhood consensus},
  url          = {http://dx.doi.org/10.1016/j.ins.2017.12.034},
  volume       = {433-434},
  year         = {2018},
}

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