SCUT-DS: Methodologies for Learning in Imbalanced Data Streams
The automation of most of our activities has led to the continuous production of data that arrive in the form of fast-arriving streams. In a supervised learning setting, instances in these streams are labeled as belonging to a particular class. When the number of classes in the data stream is more t...
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| Language: | en |
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Université d'Ottawa / University of Ottawa
2018
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| Online Access: | http://hdl.handle.net/10393/37243 http://dx.doi.org/10.20381/ruor-21515 |