A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
This is an incremental review paper that synthesizes existing knowledge on imbalanced learning for researchers in academia and industry.
This paper reviews 258 peer-reviewed papers to provide a structured analysis of machine learning techniques for handling imbalanced data, aiming to create guidelines for researchers working with large-scale imbalanced datasets.
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.