Abstract
In machine learning, the term ”class imbalanced” is frequently used. This is a crucial part of the field of machine learning. It is quite important in the classification process and has a significant impact on performance. That is why researchers are concentrating on it to overcome this difficulty. Various researchers have devised numerous methods till now. The approaches to addressing this imbalance issue found so far can be broadly categorized into three categories, which are the data-level approach, algorithm-level approach, and hybrid-level approach. To evaluate the most recent developments in resolving the negative effects of class imbalance, this study provides a comparative analysis of research that has been published within the last 5 years with an emphasis on high-class imbalance. In this study, an attempt has been made to provide a concise overview of what imbalance classification is, how it is created, and what the inconveniences are due to it. We have tried to provide a summary of several studies that have been published in the last few years and along with that a comparative analysis of all these approaches has been done.
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Study conception, design, and analysis: ZA; Draft manuscript preparation: ZA. Supervised by: SD. All authors reviewed the article and approved the final version of the manuscript.
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This article is part of the topical collection “SWOT to AI-embraced Communication Systems (SWOT-AI)” guest edited by Somnath Mukhopadhyay, Debashis De, Sunita Sarkar and Celia Shahnaz.
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Ahmed, Z., Das, S. A Comparative Analysis on Recent Methods for Addressing Imbalance Classification. SN COMPUT. SCI. 5, 30 (2024). https://doi.org/10.1007/s42979-023-02357-0
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DOI: https://doi.org/10.1007/s42979-023-02357-0