Multi-label classification using a fuzzy rough neighborhood consensus
- Author
- Sarah Vluymans (UGent) , Chris Cornelis (UGent) , Francisco Herrera and Yvan Saeys (UGent)
- Organization
- 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: http://hdl.handle.net/1854/LU-8544061
- 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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