Multiple Instance Learning With Instance-Level Positive-Unlabeled Learning in Anomaly Detection
We propose a method for learning a classifier that accurately predicts both instance and bag classes in multiple instance learning (MIL) for anomaly detection, achieving significant performance improvement. MIL, a form of weakly supervised learning, represents datasets as sets of bags labeled as eit...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11030552/ |
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