移动视界近似分支-归约方法用于深度分类树
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
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中文总结 AI 辅助
本文提出移动视界近似分支-归约方法，结合全局优化与启发式近似，高效训练大规模连续特征上的深度分类树，在精度和可扩展性上优于现有方法。
中文摘要 AI 辅助
尽管决策树对可解释性很重要，但其面临严重的可扩展性挑战。现有的全局最优方法往往受限于二元特征选择和较浅的树深度，而传统的启发式方法经常牺牲预测精度。为克服这些限制，本文提出了一种移动视界近似分支-归约方法，用于在具有连续特征的大规模数据集上训练近最优的深度分类树。该方法基于层次根-子树优化框架，通过分支-归约求解根级问题，同时使用贪婪启发式近似诱导的子树问题。尽管底层框架能够保证全局最优性，但近似（在强化学习语境中充当前瞻展开）显著提升了更深层结构的效率。随后采用低成本的移动视界策略迭代地精炼模型精度。大量数值结果表明，我们的方法在测试精度上超过现有启发式基线，同时在数据集规模和树深度方面比全局最优求解器具有显著更高的可扩展性。
英文摘要
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.
发表机构
- University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
