Ensemble learning for data stream analysis: A survey

2017/02/03 by Bartosz Krawczyk, Leandro L. Minku, João Gama +2 · 3 citations
Computer Science · #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Concept drift #Data Stream Mining Techniques #Data mining #Data stream #Data stream mining #Ensemble learning #Machine learning #Novelty #Novelty detection #Process (computing) #Streaming data #Time Series Analysis and Forecasting

paper · doi:10.1016/j.inffus.2017.02.004

openalex publication_date 2017/02/03 · crossref created 2017/02/03 · crossref issued 2017/09/01 · crossref published 2017/09/01 · crossref published-print 2017/09/01 · crossref deposited 2019/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04 · crossref indexed 2026/08/04

Citations

• Active Learning with Evolving Streaming Data
• Classifier Ensembles for Changing Environments
• Learning with Drift Detection
• CONTINUOUS INSPECTION SCHEMES
• A survey on concept drift adaptation
• Learning from Imbalanced Data
• Adaptive Mixtures of Local Experts
• An introduction to ROC analysis
• The Generalization of Student's Ratio
• Learning in the presence of concept drift and hidden contexts
• A streaming ensemble algorithm (SEA) for large-scale classification
• The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
• Incremental Learning of Concept Drift in Nonstationary Environments

Cited by

• Learning under Concept Drift: A Review
• Challenges in Benchmarking Stream Learning Algorithms with Real-world Data
• Concept drift [wikipedia]