Synthesizing class labels for highly imbalanced credit card fraud detection data
Abstract Acquiring labeled datasets often incurs substantial costs primarily due to the requirement of expert human intervention to produce accurate and reliable class labels. In the modern data landscape, an overwhelming proportion of newly generated data is unlabeled. This paradigm is especially e...
| 出版年: | Journal of Big Data |
|---|---|
| 主要な著者: | , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
SpringerOpen
2024-03-01
|
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1186/s40537-024-00897-7 |