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
Published in:IEEE access Vol. 11; pp. 45815 - 45825
Main Authors: Gutowska, Malgorzata, Little, Suzanne, Mccarren, Andrew
Format: Journal Article
Language:English
Published: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 Selection Problem (ASP), has been extensively examined in supervised classification problems, through the use of meta-learning and AutoML, however, it has received little attention in unsupervised AD tasks. This research proposes a new meta-learning approach that identifies an appropriate unsupervised AD algorithm given a set of meta-features generated from the unlabelled input dataset. The performance of the proposed meta-learner is superior to the current state of the art solution. In addition, a mixed model statistical analysis has been conducted to examine the impact of the meta-learner components: the meta-model, meta-features, and the base set of AD algorithms, on the overall performance of the meta-learner. The analysis was conducted using more than 10,000 datasets, which is significantly larger than previous studies. Results indicate that a relatively small number of meta-features can be used to identify an appropriate AD algorithm, but the choice of a meta-model in the meta-learner has a considerable impact.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3274113