Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning
Abstract Fraud datasets often times lack consistent and accurate labels, and are characterized by having high class imbalance where the number of fraudulent examples are far fewer than those of normal ones. Machine learning designed for effectively detecting fraud is an important task since fraudule...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-06-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00750-3 |