TY - JOUR
T1 - Guest Editorial
T2 - Non-IID Outlier Detection in Complex Contexts
AU - Pang, Guansong
AU - Angiulli, Fabrizio
AU - Cucuringu, Mihai
AU - Liu, Huan
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. 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. The demand for advanced outlier-detection approaches to address those explicit or implicit non-IID data characteristics. Motivated by this demand, researchers organized a Special Issue in IEEE Intelligent Systems to solicit the latest advancements in this topic in October 2019.
AB - Outlier detection, also known as anomaly detection, aims at identifying data instances that are rare or significantly different from the majority of instances. 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. The demand for advanced outlier-detection approaches to address those explicit or implicit non-IID data characteristics. Motivated by this demand, researchers organized a Special Issue in IEEE Intelligent Systems to solicit the latest advancements in this topic in October 2019.
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U2 - 10.1109/MIS.2021.3072704
DO - 10.1109/MIS.2021.3072704
M3 - Review article
AN - SCOPUS:85112710702
SN - 1541-1672
VL - 36
SP - 3
EP - 4
JO - IEEE Intelligent Systems
JF - IEEE Intelligent Systems
IS - 3
M1 - 9470961
ER -