TY - JOUR

T1 - Splitting stump forests

T2 - tree ensemble compression for edge devices (extended version)

AU - Alkhoury, Fouad

AU - Buschjäger, Sebastian

AU - Welke, Pascal

PY - 2025/8/27

Y1 - 2025/8/27

N2 - We introduce Splitting Stump Forests—small ensembles of weak learners extracted from a trained random forest. The high memory consumption of random forests 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.

AB - We introduce Splitting Stump Forests—small ensembles of weak learners extracted from a trained random forest. The high memory consumption of random forests 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.

KW - Edge devices

KW - Random forests

KW - Ensemble compression

U2 - 10.1007/s10994-025-06866-2

DO - 10.1007/s10994-025-06866-2

M3 - Journal article

VL - 114

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 10

M1 - 219

ER -