{"id":32247,"date":"2026-01-21T17:01:33","date_gmt":"2026-01-21T17:01:33","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/splitting-stump-forests-tree-ensemble-compression-for-edge-devices-extended-version\/"},"modified":"2026-06-08T13:18:48","modified_gmt":"2026-06-08T13:18:48","slug":"splitting-stump-forests-tree-ensemble-compression-for-edge-devices-extended-version","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/splitting-stump-forests-tree-ensemble-compression-for-edge-devices-extended-version\/","title":{"rendered":"Splitting Stump Forests: Tree Ensemble Compression for Edge Devices (extended version)"},"content":{"rendered":"<p>We introduce Splitting Stump Forests\u2014small 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We introduce Splitting Stump Forests\u2014small 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 [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32247","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32247","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32247\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32247"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32247"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}