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

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 di erent type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several di erent domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also signi cantly contributed to new supervised learning paradigms, including multilabel classi cation, 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 di erent software packages | from open source to commercial. In this paper, marking the fteen year anniversary of SMOTE, we re ect on the SMOTE journey, discuss the current state of a airs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.This work have been partially supported by the Spanish Ministry of Science and Technology under projects TIN2014-57251-P, TIN2015-68454-R and TIN2017-89517-P; the Project 887 BigDaP-TOOLS - Ayudas Fundaci on BBVA a Equipos de Investigaci on Cient ca 2016; and the National Science Foundation (NSF) Grant IIS-1447795

On the class overlap problem in imbalanced data classification.

Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy. First, we present a thorough experimental comparison of class overlap and class imbalance. Unlike previous work, our experiment was carried out on the full scale of class overlap and an extreme range of class imbalance degrees. Second, we provide an in-depth critical technical review of existing approaches to handle imbalanced datasets. Existing solutions from selective literature are critically reviewed and categorised as class distribution-based and class overlap-based methods. Emerging techniques and the latest development in this area are also discussed in detail. Experimental results in this paper are consistent with existing literature and show clearly that the performance of the learning algorithm deteriorates across varying degrees of class overlap whereas class imbalance does not always have an effect. The review emphasises the need for further research towards handling class overlap in imbalanced datasets to effectively improve learning algorithms’ performance