A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance

By Dina Elreedy, Amir F. Atiya. Information Sciences, 2019

682 citations recorded by the source corpus.

Abstract Imbalanced classification problems are often encountered in many applications. The challenge is that there is a minority class that has typically very little data and is often the focus of attention. One approach for handling imbalance is to generate extra data from the minority class, to overcome its shortage of data. The Synthetic Minority over-sampling TEchnique (SMOTE) is one of the dominant methods in the literature that achieves this extra sample generation. It is based on generating examples on the lines connecting a point and one its K -nearest neighbors. This paper presents a theoretical and experimental analysis of the SMOTE method. We explore the accuracy of how faithful it emulates the underlying density. To our knowledge, this is the first mathematical analysis of the SMOTE method. Moreover, we analyze the effect of the different factors on generation accuracy, such as the dimension, size of the training set and the considered number of neighbors K . We also provide a qualitative analysis that examines the factors affecting its accuracy. In addition, we explore the impact of SMOTE on classification boundary, and classification performance.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ce4a8482fc239ab78c02ac40a9a768988f34ea68

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Statistics and Probability

Junshan Zhang

Citations in the corpus

A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, neutral): “For a comprehensive analysis of the features of SMOTE refer to the work of Elreedy and Atiya (2019).”
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, background): “A salient advantage of over-sampling in low dimension space is that the accuracy in mimicking the distribution of data samples can signiﬁcantly improve when the dimension reduces [27].”
Support vector machine in structural reliability analysis: A review (2023, neutral): “Data imbalance, a common issue for the classification problem, is addressed in three ways [64] i.e. by assigning a distinct cost, modifying traditional algorithms and by data pre-processing that includes undersampling and oversampling methods [64].”