Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
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arXiv
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| Format: | Preprint |
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2020
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| _version_ | 1866929315931750400 |
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| author | Lu, Sha Liu, Lin Yu, Kui Le, Thuc Duy Liu, Jixue Li, Jiuyong |
| author_facet | Lu, Sha Liu, Lin Yu, Kui Le, Thuc Duy Liu, Jixue Li, Jiuyong |
| contents | Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a general framework that utilizes variable dependencies to uncover meaningful anomalies with better interpretability. DepAD reframes unsupervised anomaly detection as supervised feature selection and prediction tasks, which allows users to tailor anomaly detection algorithms to their specific problems and data. We extensively evaluate representative off-the-shelf techniques for the DepAD framework. Two DepAD algorithms emerge as all-rounders and superior performers in handling a wide range of datasets compared to nine state-of-the-art anomaly detection methods. Additionally, we demonstrate that DepAD algorithms provide new and insightful interpretations for detected anomalies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2011_06716 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation Lu, Sha Liu, Lin Yu, Kui Le, Thuc Duy Liu, Jixue Li, Jiuyong Machine Learning Artificial Intelligence Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a general framework that utilizes variable dependencies to uncover meaningful anomalies with better interpretability. DepAD reframes unsupervised anomaly detection as supervised feature selection and prediction tasks, which allows users to tailor anomaly detection algorithms to their specific problems and data. We extensively evaluate representative off-the-shelf techniques for the DepAD framework. Two DepAD algorithms emerge as all-rounders and superior performers in handling a wide range of datasets compared to nine state-of-the-art anomaly detection methods. Additionally, we demonstrate that DepAD algorithms provide new and insightful interpretations for detected anomalies. |
| title | Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2011.06716 |