Guest editorial: Non-IID outlier detection in complex contexts
Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. Due to its significance in many critical domains like cybersecurity, fintech, healthcare, public security, and AI safety, outlier detection...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2021
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/7018 https://ink.library.smu.edu.sg/context/sis_research/article/8021/viewcontent/Guest_Editorial_Non_IID_Outlier_Detection_in_Complex_Contexts.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. Due to its significance in many critical domains like cybersecurity, fintech, healthcare, public security, and AI safety, outlier detection has been one of the most active research areas in various communities, such as machine learning, data mining, computer vision, and statistics. Traditional outlier-detection techniques generally assume that data are independent and identically distributed (IID), which are significantly challenged in complex contexts where data are actually non-IID. These contexts are ubiquitous in not only graph data, sequence data, spatial data, temporal data, and streaming data, but also traditional multidimensional, textual, and image data.4–6 This demands for advanced outlierdetection approaches to address those explicit or implicit non-IID data characteristics. |
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