Constructing a Meta-Learner for Unsupervised Anomaly Detection

Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools. Due to the diversity of problems, no single algorithm has been found to be superior for all AD tasks. Choosing an algorithm, otherwise known as the Algorithm...

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Veröffentlicht in:	IEEE access Jg. 11; S. 45815 - 45825
Hauptverfasser:	Gutowska, Malgorzata, Little, Suzanne, Mccarren, Andrew
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	algorithm selection problem Algorithms Anomalies Anomaly detection Benchmark testing Classification algorithms Datasets Machine learning algorithms Measurement meta-features Meta-learning model selection Statistical analysis unsupervised anomaly detection Unsupervised learning
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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