# Extended Isolation Forest

arXiv: https://arxiv.org/abs/1811.02141 · HTML: https://mlsanity.com/paper/1811.02141/

## Key facts

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

## Built on by (most cited first, 30 of 81)

- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [Beyond Individual Input for Deep Anomaly Detection on Tabular Data](https://mlsanity.com/paper/2305.15121/) (2023)
- [Unsupervised Layer-wise Score Aggregation for Textual OOD Detection](https://mlsanity.com/paper/2302.09852/) (2023)
- [MemStream: Memory-Based Streaming Anomaly Detection](https://mlsanity.com/paper/2106.03837/) (2021)
- [Functional Anomaly Detection: a Benchmark Study](https://mlsanity.com/paper/2201.05115/) (2022)
- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis](https://mlsanity.com/paper/2106.11068/) (2021)
- [Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study](https://mlsanity.com/paper/2409.04834/) (2024)
- [Average Localised Proximity: A new data descriptor with good default one-class classification performance](https://mlsanity.com/paper/2101.11037/) (2021)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection](https://mlsanity.com/paper/2602.01359/) (2026)
- [ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches](https://mlsanity.com/paper/2311.12084/) (2023)
- [Retrieval Augmented Deep Anomaly Detection for Tabular Data](https://mlsanity.com/paper/2401.17052/) (2024)
- [xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection](https://mlsanity.com/paper/2506.22837/) (2025)
- [Enhancing Interpretability and Generalizability in Extended Isolation Forests](https://mlsanity.com/paper/2310.05468/) (2023)
- [Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation](https://mlsanity.com/paper/2412.02000/) (2024)
- [Anomaly detection using data depth: multivariate case](https://mlsanity.com/paper/2210.02851/) (2022)
- [Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation](https://mlsanity.com/paper/2303.11848/) (2023)
- [Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments](https://mlsanity.com/paper/2312.13896/) (2023)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [We Need to Rethink Benchmarking in Anomaly Detection](https://mlsanity.com/paper/2507.15584/) (2025)
- [Extending Isolation Forest for Anomaly Detection in Big Data via K-Means](https://mlsanity.com/paper/2104.13190/) (2021)
- [Time Series Anomaly Detection with label-free Model Selection](https://mlsanity.com/paper/2106.07473/) (2021)
- [Revisiting randomized choices in isolation forests](https://mlsanity.com/paper/2110.13402/) (2021)
- [OptIForest: Optimal Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2306.12703/) (2023)
- [Matrix Profile for Anomaly Detection on Multidimensional Time Series](https://mlsanity.com/paper/2409.09298/) (2024)
- [Synthetic Data Generation for Augmenting Small Samples](https://mlsanity.com/paper/2501.18741/) (2025)
- [TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios](https://mlsanity.com/paper/2111.15432/) (2021)

## 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.