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• W2912199514 abstract "Anomaly detection in an industrial context is a complex task. Despite very large amounts of available data, only a small fraction is labelled and anomalies are often of different nature. Moreover, implementing anomaly detection algorithms in a production environment is a costly task, therefore it is needed to unambiguously rank candidate algorithms beforehand. In this paper, we propose a methodological pipeline to evaluate anomaly detection algorithms performance in the presence of very few positive labelled data, belonging to different positive types, within an industrial context. In this pipeline, called BRIGADE (BenchmaRkInG Anomaly DEtection algorithms), we first build multiple benchmarking datasets from the limited available labelled positives and unlabelled production data. Then, we train and test candidate algorithms using a repeated 2-fold cross-validation technique. To evaluate the results, we use a metric usually employed for ranked retrieval tasks. We then aggregate all the results obtained for different benchmarking datasets into a single ranking of the candidate algorithms. We present results obtained with real industrial data, showing exposure to genuine anomalies and demonstrate cost savings." @default.
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• W2912199514 date "2018-12-01" @default.
• W2912199514 modified "2023-10-18" @default.
• W2912199514 title "Benchmarking Anomaly Detection Algorithms in an Industrial Context: Dealing with Scarce Labels and Multiple Positive Types" @default.
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• W2912199514 doi "https://doi.org/10.1109/bigdata.2018.8621956" @default.
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