doi: 10.18178/ijml.2024.14.4.1170
Borderline Active Learning: Transactional Records in Alert-Feedback System
Bokyung Amy Kwon
,
Kyungtae Kang*
- Department of Artificial Intelligence, Hanyang University, Ansan, Korea
- Manuscript receivedNovember 11, 2024
- revisedNovember 27, 2024
- acceptedDecember 6, 2024
- publishedDecember 27, 2024
Abstract
Transactional records often exhibit highly imbalanced patterns, which can hinder the performance of data-driven models in alert-feedback systems. While oversampling techniques are commonly used to address this imbalance, they increase the total number of instances, leading to higher computational costs. Although the Active Learning (AL) approach is computationally expensive, it focuses only on the most informative samples, which can be more efficient for transactional records. Our experiments show that AL outperforms SMOTE and Borderline-SMOTE in terms of accuracy and AUPRC. Therefore, AL presents a promising approach for addressing the class imbalance problem in transactional records, without the added computational burden of synthetic samples.
Keywords
- active learning
- oversampling
- transactional records
- precision-recall
- machine learning
How to Cite
Bokyung Amy Kwon and Kyungtae Kang, "Borderline Active Learning: Transactional Records in Alert-Feedback System," International Journal of Machine Learning, vol. 14, no. 4, pp. 133-139, 2024. https://doi.org/10.18178/ijml.2024.14.4.1170
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).