Splitting Stump Forests : Tree Ensemble Compression for Edge Devices

Alkhoury, Fouad and Welke, Pascal (2025) Splitting Stump Forests : Tree Ensemble Compression for Edge Devices. In: Discovery Science : DS2024. Lecture Notes in Computer Science . Springer, ITA, pp. 3-18. ISBN 9783031789793

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 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.