@inproceedings{95c084b0b9cc4e7b828d37a4ec8d56f8,
title = "Resampling Methods for Imbalanced Datasets in Multi-Label Classification: A Review",
abstract = "One of the most prevalent issues in the classification field is an imbalanced dataset. Because an instance can be assigned to more than one class label, multi-label categorization introduces complexity. The resampling technique is the most used approach to handle imbalanced problems because it is classifier-independent. The objective of this paper is to review resampling techniques that are already utilized to overcome the imbalanced issues in multi-label classification and to know what the insights and research gaps are. Some approaches are reviewed and analyzed to propose some challenges and research prospects.",
keywords = "classification, imbalanced datasets, multi-label, resampling, review",
author = "Mediana Aryuni and Chastine Fatichah and Anny Yuniarti",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024 ; Conference date: 24-05-2024 Through 25-05-2024",
year = "2024",
doi = "10.1109/ISCAIE61308.2024.10576243",
language = "English",
series = "14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "472--477",
booktitle = "14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024",
address = "United States",
}