# Mondrian Forest for Data Stream Classification Under Memory Constraints

arXiv: https://arxiv.org/abs/2205.07871 · HTML: https://mlsanity.com/paper/2205.07871/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 4 of 4)

- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Mondrian Forests: Efficient Online Random Forests](https://mlsanity.com/paper/1406.2673/) (2014)
- [A benchmark of data stream classification for human activity recognition on connected objects](https://mlsanity.com/paper/2008.11880/) (2020)
- [On-the-Fly Ensemble Pruning in Evolving Data Streams](https://mlsanity.com/paper/2109.07611/) (2021)

## Built on by (most cited first, 1 of 1)

- [Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest](https://mlsanity.com/paper/2210.05704/) (2022)

## Abstract (arXiv)

Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases. However, in the Internet of Things, this assumption is unrealistic when data comes in the form of infinite data streams, or when learning algorithms are deployed on devices with reduced amounts of memory. In this paper, we adapt the online Mondrian forest classification algorithm to work with memory constraints on data streams. In particular, we design five out-of-memory strategies to update Mondrian trees with new data points when the memory limit is reached. Moreover, we design trimming mechanisms to make Mondrian trees more robust to concept drifts under memory constraints. We evaluate our algorithms on a variety of real and simulated datasets, and we conclude with recommendations on their use in different situations: the Extend Node strategy appears as the best out-of-memory strategy in all configurations, whereas different trimming mechanisms should be adopted depending on whether a concept drift is expected. All our methods are implemented in the OrpailleCC open-source library and are ready to be used on embedded systems and connected objects.