RISSAD: Rule-based Interactive Semi-Supervised Anomaly Detection

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dc.contributor.authorDeng, Jiahaoen_US
dc.contributor.authorBrown, Eli T.en_US
dc.contributor.editorAgus, Marco and Garth, Christoph and Kerren, Andreasen_US
dc.date.accessioned2021-06-12T11:03:16Z
dc.date.available2021-06-12T11:03:16Z
dc.date.issued2021
dc.description.abstractAnomaly detection has gained increasing attention from researchers in recent times. Owing to a lack of reliable ground-truth labels, many current state-of-art techniques focus on unsupervised learning, which lacks a mechanism for user involvement. Further, these techniques do not provide interpretable results in a way that is understandable to the general public. To address this problem, we present RISSAD: an interactive technique that not only helps users to detect anomalies, but automatically characterizes those anomalies with descriptive rules. The technique employs a semi-supervised learning approach based on an algorithm that relies on a partially-labeled dataset. Addressing the need for feedback and interpretability, the tool enables users to label anomalies individually or in groups, using visual tools. We demonstrate the tool's effectiveness using quantitative experiments simulated on existing anomaly-detection datasets, and a usage scenario that illustrates a real-world application.en_US
dc.description.sectionheadersMachine Learning and SciVis Applications
dc.description.seriesinformationEuroVis 2021 - Short Papers
dc.identifier.doi10.2312/evs.20211050
dc.identifier.isbn978-3-03868-143-4
dc.identifier.pages25-29
dc.identifier.urihttps://doi.org/10.2312/evs.20211050
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20211050
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectInteractive systems
dc.subjectPattern analysis
dc.titleRISSAD: Rule-based Interactive Semi-Supervised Anomaly Detectionen_US
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