SMOTE for Learning from Imbalanced Data: Progress and Challenges. Marking the 15-year Anniversary∗

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm has been established as a “de facto” standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, it has proven successful in a number of different applications. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also made its way to new classification paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages — from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we discuss the current state of affairs with SMOTE, its application, and also identify the next set of challenges to extend SMOTE for Big Data problems.