MOA is the most popular open source framework for data stream mining, with a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
MOA performs BIG DATA stream mining in real time, and large scale machine learning. MOA can be extended with new mining algorithms, and new stream generators or evaluation measures. The goal is to provide a benchmark suite for the stream mining community.
The VFC-SMOTE algorithm is in the moa/src/main/java/moa/classifiers/meta/imbalanced folder.
If you want to refer to VFC-SMOTE in a publication, please cite the following paper:
Bernardo, Alessio, and Emanuele Della Valle. "VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams." Data Mining and Knowledge Discovery (2021): 1-35. DOI: https://doi.org/10.1007/s10618-021-00786-0