mlsanityData to 14 Oct 2026

Extended Isolation Forest

arXiv 1811.02141 ↗First posted 6 Nov 2018cs.LG astro-ph.IM stat.MLcited by 81

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Abstract (arXiv)

We present an extension to the model-free anomaly detection algorithm, Isolation Forest. This extension, named Extended Isolation Forest (EIF), resolves issues with assignment of anomaly score to given data points. We motivate the problem using heat maps for anomaly scores. These maps suffer from artifacts generated by the criteria for branching operation of the binary tree. We explain this problem in detail and demonstrate the mechanism by which it occurs visually. We then propose two different approaches for improving the situation. First we propose transforming the data randomly before creation of each tree, which results in averaging out the bias. Second, which is the preferred way, is to allow the slicing of the data to use hyperplanes with random slopes. This approach results in remedying the artifact seen in the anomaly score heat maps. We show that the robustness of the algorithm is much improved using this method by looking at the variance of scores of data points distributed along constant level sets. We report AUROC and AUPRC for our synthetic datasets, along with real-world benchmark datasets. We find no appreciable difference in the rate of convergence nor in computation time between the standard Isolation Forest and EIF.

Key facts. Cited by 81 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 8; 2022: 9; 2023: 14; 2024: 13; 2025: 21; 2026: 16.

81 papers.

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  1. 2022Deep Isolation Forest for Anomaly Detectioncited by 92
  2. 2023Unsupervised anomaly detection algorithms on real-world data: how many do we need?cited by 20
  3. 2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 19
  4. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 16
  5. 2023Unsupervised Layer-wise Score Aggregation for Textual OOD Detectioncited by 15
  6. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  7. 2022Functional Anomaly Detection: a Benchmark Studycited by 10
  8. 2024Anomaly Detection Based on Isolation Mechanisms: A Surveycited by 10
  9. 2021Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysiscited by 8
  10. 2024Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Studycited by 8
  11. 2021Average Localised Proximity: A new data descriptor with good default one-class classification performancecited by 7
  12. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
38 more
  1. 2026PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detectioncited by 7
  2. 2023ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patchescited by 6
  3. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Datacited by 6
  4. 2025xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detectioncited by 6
  5. 2023Enhancing Interpretability and Generalizability in Extended Isolation Forestscited by 5
  6. 2024Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptationcited by 5
  7. 2022Anomaly detection using data depth: multivariate casecited by 4
  8. 2023Dens-PU: PU Learning with Density-Based Positive Labeled Augmentationcited by 4
  9. 2023Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Paymentscited by 4
  10. 2025Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniquescited by 4
  11. 2025We Need to Rethink Benchmarking in Anomaly Detectioncited by 4
  12. 2021Extending Isolation Forest for Anomaly Detection in Big Data via K-Meanscited by 3
  13. 2021Time Series Anomaly Detection with label-free Model Selectioncited by 3
  14. 2021Revisiting randomized choices in isolation forestscited by 3
  15. 2023OptIForest: Optimal Isolation Forest for Anomaly Detectioncited by 3
  16. 2024Matrix Profile for Anomaly Detection on Multidimensional Time Seriescited by 3
  17. 2025Synthetic Data Generation for Augmenting Small Samplescited by 3
  18. 2021TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarioscited by 2
  19. 2022Improved Anomaly Detection by Using the Attention-Based Isolation Forestcited by 2
  20. 2022InQMAD: Incremental Quantum Measurement Anomaly Detectioncited by 2
  21. 2022AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithmcited by 2
  22. 2023A Functional Data Perspective and Baseline On Multi-Layer Out-of-Distribution Detectioncited by 2
  23. 2024AcME-AD: Accelerated Model Explanations for Anomaly Detectioncited by 2
  24. 2024Critical Review for One-class Classification: recent advances and the reality behind themcited by 2
  25. 2025PIF: Anomaly detection via preference embeddingcited by 2
  26. 2025Randomized PCA Forest for Unsupervised Outlier Detectioncited by 2
  27. 2022Learning Invariant Rules from Data for Interpretable Anomaly Detectioncited by 1
  28. 2023Unsupervised Ensemble Methods for Anomaly Detection in PLC-based Process Controlcited by 1
  29. 2024siForest: Detecting Network Anomalies with Set-Structured Isolation Forestcited by 1
  30. 2025Theoretical Investigation on Inductive Bias of Isolation Forestcited by 1
  31. 2025A Mathematical Optimization Approach to Multisphere Support Vector Data Descriptioncited by 1
  32. 2026Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasetscited by 1
  33. 2026Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detectioncited by 1
  34. 2021Isolation forests: looking beyond tree depthcited by 0
  35. 2022Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detectioncited by 0
  36. 2022Rare Yet Popular: Evidence and Implications from Labeled Datasets for Network Anomaly Detectioncited by 0
  37. 2023Unsupervised Anomaly Detection via Nonlinear Manifold Learningcited by 0
  38. 2023Distribution and volume based scoring for Isolation Forestscited by 0

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