VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

DOI10.1007/S10618-021-00786-0zbMath1485.68212OpenAlexW3200267594MaRDI QIDQ2066665

Alessio Bernardo, Emanuele Della Valle

Publication date: 14 January 2022

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-021-00786-0

zbMATH Keywords

balancingconcept driftSMLdata sketchingevolving data stream

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Sampling theory, sample surveys (62D05) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• UCI-ml
• MOA
• SMOTE
• ADASYN

Cites Work

• On evaluating stream learning algorithms
• A survey on concept drift adaptation
• Advances in Artificial Intelligence – SBIA 2004
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