Detecting Outliers in Non-IID Data: A Systematic Literature Review

Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and identically distributed (IID) assumption. This pres...

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Veröffentlicht in:	IEEE access 2023, Vol.11, p.70333-70352
Hauptverfasser:	Siddiqi, Shafaq, Qureshi, Faiza, Lindstaedt, Stefanie, Kern, Roman
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomaly detection Behavioral sciences Couplings Data analysis data dependency Data models Datasets Feature extraction heterogeneous data Literature reviews non-IID data Outlier detection Outliers (statistics) Supervised learning Taxonomy Unsupervised learning
Online-Zugang:	Volltext
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