Adaptive random forests for evolving data stream classification
Pontifícia Universidade Católica do Paraná · Télécom Paris · +7 more institutions
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Abstract
No abstract available for this paper.
Citation impact
757
total citations
- FWCI
- 33.92
- Percentile
- 100%
- References
- 55
Citations per year
Authors
8- HMHeitor Murilo GomesCorresponding
Pontifícia Universidade Católica do Paraná
- ABAlbert Bifet
Télécom Paris, Pontifícia Universidade Católica do Paraná, Université Paris-Saclay, Laboratoire Traitement et Communication de l’Information
- JRJesse Read
Télécom Paris, Université Paris-Saclay, Laboratoire d'Informatique de l'École Polytechnique, Laboratoire Traitement et Communication de l’Information, Aalto University
- JPJean Paul Barddal
Pontifícia Universidade Católica do Paraná
- FEFabrício Enembreck
Pontifícia Universidade Católica do Paraná
Topics & keywords
Topics
Keywords
- Random forest
- Computer science
- Resampling
- Data stream mining
- Boosting (machine learning)
- Machine learning
- Data stream
- Context (archaeology)
UN Sustainable Development Goals
- Life in Land
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