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Fuzzy rough classifiers for class imbalanced multi-instance data

(2016) PATTERN RECOGNITION. 53. p.36-45
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Keywords
multi-instance learning, imbalanced data, fuzzy rough set theory, CLASSIFICATION, SETS, PREDICTION, OPERATORS, TAXONOMY

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Citation

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Chicago
Vluymans, Sarah, Dánel Sánchez Tarragó, Yvan Saeys, Chris Cornelis, and Francisco Herrera. 2016. “Fuzzy Rough Classifiers for Class Imbalanced Multi-instance Data.” Pattern Recognition 53: 36–45.
APA
Vluymans, S., Sánchez Tarragó, D., Saeys, Y., Cornelis, C., & Herrera, F. (2016). Fuzzy rough classifiers for class imbalanced multi-instance data. PATTERN RECOGNITION, 53, 36–45.
Vancouver
1.
Vluymans S, Sánchez Tarragó D, Saeys Y, Cornelis C, Herrera F. Fuzzy rough classifiers for class imbalanced multi-instance data. PATTERN RECOGNITION. 2016;53:36–45.
MLA
Vluymans, Sarah, Dánel Sánchez Tarragó, Yvan Saeys, et al. “Fuzzy Rough Classifiers for Class Imbalanced Multi-instance Data.” PATTERN RECOGNITION 53 (2016): 36–45. Print.
@article{7079638,
  author       = {Vluymans, Sarah and S{\'a}nchez Tarrag{\'o}, D{\'a}nel and Saeys, Yvan and Cornelis, Chris and Herrera, Francisco},
  issn         = {0031-3203},
  journal      = {PATTERN RECOGNITION},
  keyword      = {multi-instance learning,imbalanced data,fuzzy rough set theory,CLASSIFICATION,SETS,PREDICTION,OPERATORS,TAXONOMY},
  language     = {eng},
  pages        = {36--45},
  title        = {Fuzzy rough classifiers for class imbalanced multi-instance data},
  url          = {http://dx.doi.org/10.1016/j.patcog.2015.12.002},
  volume       = {53},
  year         = {2016},
}

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