Challenges and limitations of synthetic minority oversampling techniques in machine learning

Abstract

Oversampling is the most utilized approach to deal with class-imbalanced datasets, as seen by the plethora of oversampling methods developed in the last two decades. We argue in the following editorial the issues with oversampling that stem from the possibility of overfitting and the generation of synthetic cases that might not accurately represent the minority class. These limitations should be considered when using oversampling techniques. We also propose several alternate strategies for dealing with imbalanced data, as well as a future work perspective.

Keywords: Machine learning; Class imbalance; Overfitting; Misdiagnosis

Core Tip: Addressing class imbalance in medical datasets, particularly in the context of machine learning applications, requires a cautious approach. While oversampling methods like synthetic minority oversampling technique are commonly used, it is crucial to recognize their limitations. They may introduce synthetic instances that do not accurately represent the minority class, potentially leading to overfitting and unreliable results in real-world medical scenarios. Instead, we can consider exploring alternative approaches such as Ensemble Learning-Based Methods like XGBoost and Easy Ensemble which have shown promise in mitigating bias and providing more robust performance. Collaborating with data science specialists and medical professionals to design and validate these techniques is essential to ensure their reliability and effectiveness in medical applications.