Bayesian active learning isolation forest (B-ALIF): A weakly supervised strategy for anomaly detection

Author:

Sartor DavideORCID,Barbariol TommasoORCID,Susto Gian AntonioORCID

Funder

Ministero dell'Istruzione dell'Università e della Ricerca

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference30 articles.

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4. Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J., 2000. LOF: Identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. pp. 93–104.

5. Weakly supervised anomaly detection based on two-step cyclic iterative PU learning strategy;Chen;Neural Process. Lett.,2022

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Extended B-ALIF: Improving Anomaly Detection with Human Feedback;2024 32nd Mediterranean Conference on Control and Automation (MED);2024-06-11

2. Reliability Evaluation of Advanced Metering Infrastructure Based on Isolation Forest Anomalies Detection;2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS);2024-02-21

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