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

TLDR

SMOTE is the de facto standard for handling class imbalance, praised for its simplicity, robustness, and wide adoption across domains and software packages. This paper reviews SMOTE’s fifteen‑year evolution, current applications, and outlines future challenges for scaling it to Big Data. The authors reflect on SMOTE’s journey, discuss its present state and applications, and identify next steps for extending it to Big Data problems.

Abstract

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "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, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning 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 reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.