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

By Alberto Fernández, Salvador Garcia, Francisco Herrera, Nitesh V. Chawla. 2018

2,068 citations recorded by the source corpus.

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 reect 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Varun Prashant Gangal
2. Soroush Vosoughi
3. Eduard H. Hovy
4. Teruko Mitamura
5. Jason Wei
6. Steven Y. Feng
7. Sarath Chandar
8. Jay H. Lee

In Electrical and Electronic Engineering

1. Eduard H. Hovy
2. Amir F. Atiya
3. Rohitash Chandra
4. Azal Ahmad Khan
5. Omkar Chaudhari

In Optimization & Training

1. Jason Wei
2. Sarath Chandar
3. Eduard H. Hovy
4. Jay H. Lee
5. Steven Y. Feng
6. Amir F. Atiya
7. Teruko Mitamura
8. Rohitash Chandra

Citations in the corpus

• A Survey of Data Augmentation Approaches for NLP (2021, background): “Xia et al. (2019) (Fernández et al., 2018).”
• Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook (2020, background): “While SMOTE and its extensions have achieved considerable results in recent years [79], studies have shown that for high-dimensionality problems its effects are underwhelming [80].”
• A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, positive): “SMOTE is a powerful over-sampling method that has been widely adopted in many applications (Fernández et al., 2018; Ahsan et al., 2018; Kishor & Chakraborty, 2021).”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Furthermore, a number of alterations of SMOTE, such as geometric SMOTE [212] and weighted SMOTE [213] exist.”