A Review of Machine Learning Techniques in Imbalanced Data and Future Trends

• URL: http://arxiv.org/abs/2310.07917v1
• Date: Wed, 11 Oct 2023 22:14:17 GMT
• Title: A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
• Authors: Elaheh Jafarigol, Theodore Trafalis
• Abstract summary: We have collected and reviewed 258 peer-reviewed papers from archival journals and conference papers. This work aims to provide a structured review of methods used to address the problem of imbalanced data in various domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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