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- W2894846849 abstract "We propose an algorithm called GLAD (GLocalized Anomaly Detection) that allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback. The key idea is to place a uniform prior on the relevance of each member of the anomaly detection ensemble over the input feature space via a neural network trained on unlabeled instances, and tune the weights of the neural network to adjust the local relevance of each ensemble member using all labeled instances. Our experiments on synthetic and real-world data show the effectiveness of GLAD in learning the local relevance of ensemble members and discovering anomalies via label feedback." @default.
- W2894846849 created "2018-10-12" @default.
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- W2894846849 date "2018-10-02" @default.
- W2894846849 modified "2023-09-27" @default.
- W2894846849 title "GLAD: GLocalized Anomaly Detection via Active Feature Space Suppression." @default.
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- W2894846849 hasPublicationYear "2018" @default.
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