[Submitted on 18 Sep 2026]
Title:A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
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Abstract:Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by binary feature selection and shallow tree depths, whereas traditional heuristic approaches frequently sacrifice predictive accuracy. To overcome these limitations, this paper proposes a moving-horizon approximate branch-and-reduce method to train near-optimal deep classification trees on large-scale datasets with continuous features. Built on a hierarchical root-subtree optimization framework, the method solves the root-level problem via branch-and-reduce while approximating the induced subtree problem using greedy heuristics. Although the underlying framework is capable of guaranteeing global optimality, the approximation, which functions as a lookahead rollout in a reinforcement learning context, significantly boosts efficiency for deeper structures. A low-cost moving-horizon strategy is then employed to iteratively refine model accuracy. Extensive numerical results demonstrate that our method exceeds the testing accuracy of existing heuristic baselines while offering significantly greater scalability, in terms of both dataset size and tree depth, than global optimal solvers.
Comments: J2C Certification
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
Cite as: arXiv:2609.38194 [cs.LG]
(or arXiv:2609.38194v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.38194
arXiv-issued DOI via DataCite
Journal reference: Transactions on Machine Learning Research, August 2026
Submission history
From: Chenxuanyin Zou [view email] [v1] Fri, 18 Sep 2026 05:50:36 UTC (763 KB)
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