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DEXA
2008
Springer

Classifying Evolving Data Streams Using Dynamic Streaming Random Forests

13 years 5 months ago
Classifying Evolving Data Streams Using Dynamic Streaming Random Forests
We consider the problem of data-stream classification, introducing a stream-classification algorithm, Dynamic Streaming Random Forests, that is able to handle evolving data streams using an entropy-based drift-detection technique. The algorithm automatically adjusts its parameters based on the data seen so far. Experimental results show that the algorithm handles multi-class problems for which the underlying class boundaries drift, without losing accuracy.
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DEXA
Authors Hanady Abdulsalam, David B. Skillicorn, Patrick Martin
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