Isolation Forest

Ask about this topic

Overview

Isolation Forest is an unsupervised method for finding unusual data points when labeled examples are scarce. Its core approach builds trees from random feature splits and treats observations isolated in fewer splits as more anomalous. Extensions add monotonic distance rules, local-density or clustering signals, time-series features, and explanations, and are tested on tabular benchmarks such as ADBench as well as fraud, industrial, and environmental data. A central challenge is that axis-parallel splits and score thresholds can miss some anomaly patterns, while improvements often depend on the dataset and operating context.

Latest papers

CardsList

11 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

  1. ReForest: Interpretability Meets Reasoning in Isolation Forest

    Oct 27, 2026Juyoung Seo, Geonwoo Bang, Dongho Kim +1Anomaly DetectionInterpretable Anomaly Detection

    Department of Computer Education, Sungkyunkwan University, Seoul, Korea · Department of Immersive Media Engineering, Sungkyunkwan University, Seoul, Korea · Department of Computer Education/Social Innovation Convergence Program, Sungkyunkwan University, Seoul, Korea

  2. RIFT: Relative Isolation From Trees For Anomaly Detection

    Oct 8, 2026Mark Daniel Szalai, Gabor HorvathUnsupervised Anomaly DetectionAnomaly Detection

    Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, M˝uegyetem rkp. 3, Budapest, H-1111, Hungary

  3. Towards an approach to multivariate outlier detection for District Heating System data

    Aug 11, 2026Rajko Turudija, Dušan Stojiljković, Milan Zdravković +1Isolation ForestIndustrial Anomaly Detection

    Faculty of Mechanical Engineering University of Niš ul. Aleksandra Medvedeva 14, 18000 Niš, Serbia

  4. Unsupervised Detection of Groundwater Storage Anomalies in Ghana Using GRACE Satellite Data

    Aug 10, 2026George Yamoah Afrifa, Theophilus Ansah-Narh, Marcellin AtemkengIsolation ForestAnomaly Detection

    Ghana Space Science and Technology Institute Accra, Ghana · Department of Mathematics Rhodes University, Grahamstown, South Africa

  5. Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective

    Jul 15, 2026Anupa LodhiExplainable Artificial IntelligenceIsolation Forest

    Department of Computer Science and Engineering · SAGE University Indore, India

  6. CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection

    Jun 11, 2026William SmitsIsolation ForestInterpretable Anomaly Detection

    Avathon, Austin, TX, USA

  7. SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection

    May 21, 2026Venkatakrishnan GopalakrishnanTabular Anomaly DetectionIsolation Forest

    Independent Researcher Redmond, WA, USA

  8. Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment

    Apr 29, 2026Isaac Tettey Adjokatse, Samuel Senyo Koranteng, George Yamoah Afrifa +6Isolation ForestUnsupervised Anomaly Detection

    Institute of Environment and Sanitation Studies University of Ghana Legon, Accra, Ghana · Ghana Space Science and Technology Institute Accra, Ghana · Department of Mathematics Rhodes University, Grahamstown, South Africa +2

  9. Hybrid Autoencoder-Isolation Forest approach for time series anomaly detection in C70XP cyclotron operation data at ARRONAX

    Mar 20, 2026F Basbous, F Poirier, F Haddad +1Time-Series Anomaly DetectionIndustrial Anomaly Detection

    Nantes Univ, GIP ARRONAX · ARRONAX, Saint Herblain, France · also at Nantes Université, Nantes, France +5

  10. Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection

    Oct 15, 2025Yang Cao, Sikun Yang, Hao Tian +5Tabular Anomaly DetectionIsolation Forest

    Tsinghua Shenzhen International Graduate School, Tsinghua University, China · School of Computing and Information Technology, Great Bay University, China · School of Compuer Science, Nanjing University, China +3

  11. Monotonic anomaly detection

    Oct 30, 2024Oliver Urs Lenz, Matthijs van LeeuwenIsolation ForestSemi-Supervised Anomaly Detection

    Department of Statistics, Data Science and Modelling, National Institute for Public Health and the Environment, The Netherlands · Leiden Institute of Advanced Computer Science, Leiden University · Department of Applied Mathematics, Computer Science and Statistics, Ghent University