  Learning classifiers using skewed or imbalanced datasets can occasionally
lead to classification issues; this is a serious issue. In some cases, one
class contains the majority of examples while the other, which is frequently
the more important class, is nevertheless represented by a smaller proportion
of examples. Using this kind of data could make many carefully designed
machine-learning systems ineffective. High training fidelity was a term used to
describe biases vs. all other instances of the class. The best approach to all
possible remedies to this issue is typically to gain from the minority class.
The article examines the most widely used methods for addressing the problem of
learning with a class imbalance, including data-level, algorithm-level, hybrid,
cost-sensitive learning, and deep learning, etc. including their advantages and
limitations. The efficiency and performance of the classifier are assessed
using a myriad of evaluation metrics.
