Splitting Stump Forests: Tree Ensemble Compression for Edge Devices
We introduce Splitting Stump Forests – small ensembles of weak learners extracted from a trained random forest. The high memory consumption of random forest ensemble models renders them unfit for resource-constrained devices. We show empirically that we can significantly reduce the model size and inference time by selecting nodes that evenly split the arriving training data and applying a linear model on the resulting representation. Our extensive empirical evaluation indicates that Splitting Stump Forests outperform random forests and state-of-the-art compression methods on memory-limited embedded devices.
- Veröffentlicht in:
Discovery Science - Typ:
Inproceedings - Autoren:
Alkhoury, Fouad; Welke, Pascal - Jahr:
2025
Informationen zur Zitierung
Alkhoury, Fouad; Welke, Pascal: Splitting Stump Forests: Tree Ensemble Compression for Edge Devices, Discovery Science, 2025, 3--18, Springer Nature Switzerland, Alkhoury.Welke.2025a,
@Inproceedings{Alkhoury.Welke.2025a,
author={Alkhoury, Fouad; Welke, Pascal},
title={Splitting Stump Forests: Tree Ensemble Compression for Edge Devices},
booktitle={Discovery Science},
pages={3--18},
publisher={Springer Nature Switzerland},
year={2025},
abstract={We introduce Splitting Stump Forests – small ensembles of weak learners extracted from a trained random forest. The high memory consumption of random forest ensemble models renders them unfit for resource-constrained devices. We show empirically that we can significantly reduce the model size and inference time by selecting nodes that evenly split the arriving training data and applying a...}}
