# Isolation Mondrian Forest for Batch and Online Anomaly Detection

arXiv: https://arxiv.org/abs/2003.03692 · HTML: https://mlsanity.com/paper/2003.03692/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Inference with Mondrian Random Forests](https://mlsanity.com/paper/2310.09702/) (2023)
- [TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios](https://mlsanity.com/paper/2111.15432/) (2021)
- [MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection](https://mlsanity.com/paper/2510.26643/) (2025)
- [Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection](https://mlsanity.com/paper/2610.20243/) (2026)

## Abstract (arXiv)

We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods.