[2603.01588] Jump Like A Squirrel: Optimized Execution Step Order for Anytime Random Forest Inference

Computer Science > Machine Learning arXiv:2603.01588 (cs) [Submitted on 2 Mar 2026] Title:Jump Like A Squirrel: Optimized Execution Step Order for Anytime Random Forest Inference Authors:Daniel Biebert, Christian Hakert, Kay Heider, Daniel Kuhse, Sebastian Buschjäger, Jian-Jia Chen View a PDF of the paper titled Jump Like A Squirrel: Optimized Execution Step Order for Anytime Random Forest Inference, by Daniel Biebert and 5 other authors View PDF HTML (experimental) Abstract:Due to their efficiency and small size, decision trees and random forests are popular machine learning models used for classification on resource-constrained systems. In such systems, the available execution time for inference in a random forest might not be sufficient for a complete model execution. Ideally, the already gained prediction confidence should be retained. An anytime algorithm is designed to be able to be aborted anytime, while giving a result with an increasing quality over time. Previous approaches have realized random forests as anytime algorithms on the granularity of trees, stopping after some but not all trees of a forest have been executed. However, due to the way decision trees subdivide the sample space in every step, an increase in prediction quality is achieved with every additional step in one tree. In this paper, we realize decision trees and random forest as anytime algorithms on the granularity of single steps in trees. This approach opens a design space to define the step o...