ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams

DOI10.1007/S10994-022-06168-XOpenAlexW4224267307MaRDI QIDQ2102324

Bartosz Krawczyk, Alberto Cano

Publication date: 28 November 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06168-x

zbMATH Keywords

online learningconcept driftdata streamsimbalanced datacontinual learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

A dynamic similarity weighted evolving fuzzy system for concept drift of data streams

Uses Software

• MOA

Cites Work

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• Combining block-based and online methods in learning ensembles from concept drifting data streams
• Tensor decision trees for continual learning from drifting data streams
• Kappa updated ensemble for drifting data stream mining
• Data Mining in Time Series and Streaming Databases
• A survey on concept drift adaptation

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