A Review of Machine Learning Techniques in Imbalanced Data and Future
  Trends
Elaheh Jafarigol Theodore Trafalis
For over two decades, detecting rare events has been a challenging task among
researchers in the data mining and machine learning domain. Real-life problems
inspire researchers to navigate and further improve data processing and
algorithmic approaches to achieve effective and computationally efficient
methods for imbalanced learning. In this paper, we have collected and reviewed
258 peer-reviewed papers from archival journals and conference papers in an
attempt to provide an in-depth review of various approaches in imbalanced
learning from technical and application perspectives. This work aims to provide
a structured review of methods used to address the problem of imbalanced data
in various domains and create a general guideline for researchers in academia
or industry who want to dive into the broad field of machine learning using
large-scale imbalanced data.