# Enhancing Interpretability and Generalizability in Extended Isolation Forests

arXiv: https://arxiv.org/abs/2310.05468 · HTML: https://mlsanity.com/paper/2310.05468/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Evaluation of post-hoc interpretability methods in time-series classification](https://mlsanity.com/paper/2202.05656/) (2022)
- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [Feature Importance Ranking for Deep Learning](https://mlsanity.com/paper/2010.08973/) (2020)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [Unsupervised anomaly detection algorithms on real-world data: how many do we need?](https://mlsanity.com/paper/2305.00735/) (2023)
- [A new interpretable unsupervised anomaly detection method based on residual explanation](https://mlsanity.com/paper/2103.07953/) (2021)
- [An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination](https://mlsanity.com/paper/2309.09436/) (2023)

## Built on by (most cited first, 5 of 5)

- [AcME-AD: Accelerated Model Explanations for Anomaly Detection](https://mlsanity.com/paper/2403.01245/) (2024)
- [Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest](https://mlsanity.com/paper/2505.04019/) (2025)
- [Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI](https://mlsanity.com/paper/2405.01158/) (2024)
- [Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection](https://mlsanity.com/paper/2511.06054/) (2025)
- [Does Explainability Survive Data Drift?](https://mlsanity.com/paper/2610.05379/) (2026)

## Abstract (arXiv)

Anomaly Detection (AD) focuses on identifying unusual behaviors in complex datasets. Machine Learning (ML) algorithms and Decision Support Systems (DSSs) provide effective solutions for AD, but detecting anomalies alone may not be enough, especially in engineering, where diagnostics and maintenance are crucial. Users need clear explanations to support root cause analysis and build trust in the model. The unsupervised nature of AD, however, makes interpretability a challenge. This paper introduces Extended Isolation Forest Feature Importance (ExIFFI), a method that explains predictions made by Extended Isolation Forest (EIF) models, which split data using hyperplanes. ExIFFI provides explanations at both global and local levels by leveraging feature importance. We also present an improved version, Enhanced Extended Isolation Forest (EIF+), designed to enhance the model's ability to detect unseen anomalies through a revised splitting strategy. Using five synthetic and eleven real-world datasets, we conduct a comparative analysis, evaluating unsupervised AD methods with the Average Precision metric. EIF+ consistently outperforms EIF across all datasets when trained without anomalies, demonstrating better generalization. To assess ExIFFI's interpretability, we introduce the Area Under the Curve of Feature Selection (AUCFS), a novel metric using feature selection as a proxy task. ExIFFI outperforms other unsupervised interpretability methods on 8 of 11 real-world datasets and successfully identifies anomalous features in synthetic datasets. When trained only on inliers, ExIFFI also outperforms competing models on real-world data and accurately detects anomalous features in synthetic datasets. We provide open-source code to encourage further research and reproducibility.