On the use of MapReduce for imbalanced big data using Random Forest

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A survey on addressing high-class imbalance in big data (2018, methodology): “The more popular computational frameworks for working with big data include Apache Spark [16], MapReduce (original), and Apache Hadoop [46].”
Learning from imbalanced data: open challenges and future directions (2016, methodology): “– SMOTE-based oversampling methods applied in distributed environments such as MapReduce tend to fail [13].”
Random Forests for Big Data (2015, methodology): “One variant relies on subsampling while three others are related to parallel implementations of random forests and involve either various adaptations of bootstrap to Big Data or “divide-and-conquer” approaches.”
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (2017, methodology): “In [6], [7], some machine learning algorithms were proposed based on the MapReduce model.”
Application of machine learning to construction injury prediction (2016, methodology): “To address class imbalance for the RF models, we used stratified oversampling [16,21].”
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “Among all research studies, we must first emphasize the one carried out in (Ŕıo et al., 2014) in which the first SMOTE adaptation to Big Data was adapted to the MapReduce work-flow.”