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PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams
In order to adapt random forests to the dynamic nature of data streams, the state-of-the-art technique discards trained trees and grows new trees when concept drifts are detected. This is particularly wasteful when recurrent patterns exist. In this work, we introduce a novel framework called PEARL,...
Autores principales: | Wu, Ocean, Koh, Yun Sing, Dobbie, Gillian, Lacombe, Thomas |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206241/ http://dx.doi.org/10.1007/978-3-030-47436-2_2 |
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