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EPRENNID : an evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

Sarah Vluymans (UGent) , Isaac Triguero (UGent) , Chris Cornelis (UGent) and Yvan Saeys (UGent)
(2016) NEUROCOMPUTING. 216. p.596-610
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Keywords
Imbalanced data, Prototype selection, Prototype generation, Differential evolution, Nearest neighbor, OVER-SAMPLING TECHNIQUE, DIFFERENTIAL EVOLUTION, INSTANCE SELECTION, DATA-SETS, ALGORITHMS, SMOTE, OPTIMIZATION, CLASSIFIERS, PERFORMANCE

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

Chicago
Vluymans, Sarah, Isaac Triguero, Chris Cornelis, and Yvan Saeys. 2016. “EPRENNID : an Evolutionary Prototype Reduction Based Ensemble for Nearest Neighbor Classification of Imbalanced Data.” Neurocomputing 216: 596–610.
APA
Vluymans, S., Triguero, I., Cornelis, C., & Saeys, Y. (2016). EPRENNID : an evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data. NEUROCOMPUTING, 216, 596–610.
Vancouver
1.
Vluymans S, Triguero I, Cornelis C, Saeys Y. EPRENNID : an evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data. NEUROCOMPUTING. 2016;216:596–610.
MLA
Vluymans, Sarah et al. “EPRENNID : an Evolutionary Prototype Reduction Based Ensemble for Nearest Neighbor Classification of Imbalanced Data.” NEUROCOMPUTING 216 (2016): 596–610. Print.
@article{8500185,
  author       = {Vluymans, Sarah and Triguero, Isaac and Cornelis, Chris and Saeys, Yvan},
  issn         = {0925-2312},
  journal      = {NEUROCOMPUTING},
  keywords     = {Imbalanced data,Prototype selection,Prototype generation,Differential evolution,Nearest neighbor,OVER-SAMPLING TECHNIQUE,DIFFERENTIAL EVOLUTION,INSTANCE SELECTION,DATA-SETS,ALGORITHMS,SMOTE,OPTIMIZATION,CLASSIFIERS,PERFORMANCE},
  language     = {eng},
  pages        = {596--610},
  title        = {EPRENNID : an evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data},
  url          = {http://dx.doi.org/10.1016/j.neucom.2016.08.026},
  volume       = {216},
  year         = {2016},
}

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