  For the last two decades, oversampling has been employed to overcome the
challenge of learning from imbalanced datasets. Many approaches to solving this
challenge have been offered in the literature. Oversampling, on the other hand,
is a concern. That is, models trained on fictitious data may fail spectacularly
when put to real-world problems. The fundamental difficulty with oversampling
approaches is that, given a real-life population, the synthesized samples may
not truly belong to the minority class. As a result, training a classifier on
these samples while pretending they represent minority may result in incorrect
predictions when the model is used in the real world. We analyzed a large
number of oversampling methods in this paper and devised a new oversampling
evaluation system based on hiding a number of majority examples and comparing
them to those generated by the oversampling process. Based on our evaluation
system, we ranked all these methods based on their incorrectly generated
examples for comparison. Our experiments using more than 70 oversampling
methods and three imbalanced real-world datasets reveal that all oversampling
methods studied generate minority samples that are most likely to be majority.
Given data and methods in hand, we argue that oversampling in its current forms
and methodologies is unreliable for learning from class imbalanced data and
should be avoided in real-world applications.
