Challenges and limitations of synthetic minority oversampling techniques in machine learning
Ibraheem M. Alkhawaldeh, Ibrahem Albalkhi, Abdulqadir Jeprel Naswhan
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.
Topics & Concepts
OversamplingOverfittingClass (philosophy)Perspective (graphical)Machine learningComputer scienceArtificial intelligenceData scienceArtificial neural networkBandwidth (computing)Computer networkImbalanced Data Classification TechniquesMachine Learning and AlgorithmsAnomaly Detection Techniques and Applications