Constructing a Meta-Learner for Unsupervised Anomaly Detection

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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Bibliographic Details
Main Authors: Malgorzata Gutowska, Suzanne Little, Andrew Mccarren
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Anomaly detection
unsupervised anomaly detection
algorithm selection problem
model selection
meta-learning
meta-features
Online Access:https://ieeexplore.ieee.org/document/10121417/

Internet

https://ieeexplore.ieee.org/document/10121417/

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