VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams
Alessio Bernardo, Emanuele Della Valle · Data Mining and Knowledge Discovery · 2021
Abstract The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique ( VFC - SMOTE ). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline - Smote inspired by Data Sketching. We benchmarked VFC - SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC - SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.