A mathematical assessment of the isolation random forest method for anomaly detection in big data

DOI10.1002/mma.8570OpenAlexW4285731912MaRDI QIDQ6182044

Fernando A. Morales, Edgar A. Ramos, Jorge M. Ramirez

Publication date: 20 December 2023

Published in: Mathematical Methods in the Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://www.osti.gov/biblio/1881066

zbMATH Keywords

Monte Carlo methodsprobabilistic algorithmsanomaly detectionisolation random forest

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Randomized algorithms (68W20) Statistical aspects of big data and data science (62R07) Computational aspects of data analysis and big data (68T09)

Cites Work

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• Randomized search trees
• Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
• Discovering cluster-based local outliers

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