Semi-Supervised Anomaly Detection Algorithm Using Probabilistic Labeling (SAD-PL)
To detect abnormal data via semi-supervised learning, unlabeled data are generally assumed to be normal data. This assumption, however, causes inevitable performance degradation when a small fraction of abnormal data is included in the unlabeled dataset. To overcome the degradation and to maintain s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9576706/ |