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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sg-smu-ink.sis_research-80212022-03-17T15:06:23Z Guest editorial: Non-IID outlier detection in complex contexts PANG, Guansong ANGIULLI, Fabrizio CUCURINGU, Mihai LIU, Huan 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. 2021-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7018 info:doi/10.1109/MIS.2021.3072704 https://ink.library.smu.edu.sg/context/sis_research/article/8021/viewcontent/Guest_Editorial_Non_IID_Outlier_Detection_in_Complex_Contexts.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Databases and Information Systems |
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Artificial Intelligence and Robotics Databases and Information Systems PANG, Guansong ANGIULLI, Fabrizio CUCURINGU, Mihai LIU, Huan Guest editorial: Non-IID outlier detection in complex contexts |
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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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PANG, Guansong ANGIULLI, Fabrizio CUCURINGU, Mihai LIU, Huan |
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PANG, Guansong ANGIULLI, Fabrizio CUCURINGU, Mihai LIU, Huan |
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PANG, Guansong |
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Guest editorial: Non-IID outlier detection in complex contexts |
title_short |
Guest editorial: Non-IID outlier detection in complex contexts |
title_full |
Guest editorial: Non-IID outlier detection in complex contexts |
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Guest editorial: Non-IID outlier detection in complex contexts |
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Guest editorial: Non-IID outlier detection in complex contexts |
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guest editorial: non-iid outlier detection in complex contexts |
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Institutional Knowledge at Singapore Management University |
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2021 |
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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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