# PIDForest: Anomaly Detection via Partial Identification

arXiv: https://arxiv.org/abs/1912.03582 · HTML: https://mlsanity.com/paper/1912.03582/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Data-Efficient and Interpretable Tabular Anomaly Detection](https://mlsanity.com/paper/2203.02034/) (2022)
- [METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection](https://mlsanity.com/paper/2312.16831/) (2023)
- [UADB: Unsupervised Anomaly Detection Booster](https://mlsanity.com/paper/2306.01997/) (2023)
- [Anomaly Detection with Score Distribution Discrimination](https://mlsanity.com/paper/2306.14403/) (2023)
- [Learning Decision Trees as Amortized Structure Inference](https://mlsanity.com/paper/2503.06985/) (2025)
- [Exploring Global and Local Information for Anomaly Detection with Normal Samples](https://mlsanity.com/paper/2306.02025/) (2023)
- [Deep Anomaly Detection and Search via Reinforcement Learning](https://mlsanity.com/paper/2208.14834/) (2022)
- [Distribution and volume based scoring for Isolation Forests](https://mlsanity.com/paper/2309.11450/) (2023)
- [Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series](https://mlsanity.com/paper/2507.07559/) (2025)
- [Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation](https://mlsanity.com/paper/2604.14726/) (2026)
- [Witnesses Explain Anomalies](https://mlsanity.com/paper/2609.03826/) (2026)

## Abstract (arXiv)

We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. Formalizing this intuition, we propose a geometric anomaly measure for a point that we call PIDScore, which measures the minimum density of data points over all subcubes containing the point. We present PIDForest: a random forest based algorithm that finds anomalies based on this definition. We show that it performs favorably in comparison to several popular anomaly detection methods, across a broad range of benchmarks. PIDForest also provides a succinct explanation for why a point is labelled anomalous, by providing a set of features and ranges for them which are relatively uncommon in the dataset.