Anomaly detection using unsupervised machine learning algorithms: A simulation study

Anomaly detection using unsupervised machine learning algorithms: A simulation study

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Cova...

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Bibliographic Details
Main Author: Edmund Fosu Agyemang
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Scientific African
Subjects:
Anomaly detection
Unsupervised machine learning algorithms
One-class support vector machine
Isolation forest
Local outlier factor
Robust covariance
Online Access:http://www.sciencedirect.com/science/article/pii/S2468227624003284

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http://www.sciencedirect.com/science/article/pii/S2468227624003284

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