Multi-domain Active Learning for Semi-supervised Anomaly Detection

Vercruyssen, Vincent; Perini, Lorenzo; Meert, Wannes; Davis, Jesse

doi:10.1007/978-3-031-26412-2_30

Cited by 3 publications

(3 citation statements)

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“…The transfer anomaly detection methods presented in [9]- [12] make the same assumption as this work; that is, as in our method, these studies use both anomalous and normal samples in a related dataset, also known as source data, and only normal data in the data of the interest, also known as the target dataset. The algorithms presented in [10], [11] are instance-based transfer learning methods, where the source data is transferred to the target domain after estimating the distribution of both source data and the target data. After the transfer, an LOF-based anomaly detection algorithm is built using transferred source data in [10] or using both transferred source data and unlabeled target data in [11].…”

Section: B Transfer Learning In Anomaly Detectionmentioning

confidence: 99%

“…The algorithms presented in [10], [11] are instance-based transfer learning methods, where the source data is transferred to the target domain after estimating the distribution of both source data and the target data. After the transfer, an LOF-based anomaly detection algorithm is built using transferred source data in [10] or using both transferred source data and unlabeled target data in [11]. An important assumption of the methods presented in [10], [11] is that an ample amount of target data is needed to ensure accurate density estimation of the target data distribution.…”

Section: B Transfer Learning In Anomaly Detectionmentioning

confidence: 99%

“…After the transfer, an LOF-based anomaly detection algorithm is built using transferred source data in [10] or using both transferred source data and unlabeled target data in [11]. An important assumption of the methods presented in [10], [11] is that an ample amount of target data is needed to ensure accurate density estimation of the target data distribution. However, the algorithm proposed by us here does not require such an ample amount of the target dataset.…”

Section: B Transfer Learning In Anomaly Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Transfer Learning for Anomaly Detection in Rotating Machinery using Data-driven Key Order Estimation

Liang,

Shui,

Gupta

et al. 2024

Preprint

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Anomaly detection is an important task in industrial applications. However, designing an accurate anomaly detector can be very challenging in settings where anomalous labels are sparse or, in the worst case, missing in the training data. To mitigate this issue of a lack of anomalous labels in the domain of interest, existing approaches use transfer learning, leveraging information from anomalous samples in a closely related domain. Although previous studies have shown good results from applying transfer learning, they do not specifically address the issue of high false-positive rates, especially in industrial settings. High false-positive rates can arise from misleading information present in uninformative features. Inspired by this observation, the paper focuses on identifying key input features—termed as such due to their strong predictability in anomaly detection. A transfer learning approach is introduced that leverages the optimal fβ score for key feature estimation. This approach involves a weight vector to amplify key features and attenuate uninformative inputs during prediction. We demonstrate the capabilities of our proposed method through an industrial application: anomaly detection for rotating machinery. Based on our findings, anomaly detection algorithms that utilize data-driven features obtained through the proposed method outperform detectors based on features identified by domain experts. More importantly, our proposed framework can work with any downstream unsupervised anomaly detection algorithm, allowing us to freely choose the best algorithm for the anomaly detection task.

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