DICS：面向决策树分类器的数据驱动型质心分裂方法
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
- University of Texas at El Paso（德克萨斯大学埃尔帕索分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出DICS框架，通过聚类方法减少决策树训练的分裂搜索空间，可集成到多种模型中，在保持准确率的同时缩短训练时间，提升分类树学习的可扩展性。
AI中文摘要：
基于决策树的模型因具备可解释性和出色的经验性能，在机器学习领域得到广泛应用。然而，训练决策树的计算成本较高，尤其是在处理大型高维数据集时，这主要是因为每个节点都需要对候选分裂进行穷举搜索。为提升计算效率，本文提出了数据驱动型质心分裂（Data-Informed Centroid Splitting，DICS），这是一种基于聚类的框架，可利用数据驱动先验构建紧凑且信息丰富的候选分裂集合。通过融入类别感知结构，DICS能显著减少分类任务的分裂搜索空间，同时保持预测性能。本文还提供了理论分析，表明在给定假设下，与穷举分裂搜索相比，DICS不会降低分类树的性能。DICS可集成到分类树、随机森林和梯度提升模型中。大量实验表明，在合成数据集和基准数据集上，DICS在实现相当准确率的同时大幅缩短了训练时间，凸显了将数据驱动先验融入分裂选择以实现可扩展分类树学习的优势。
英文摘要：
Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance. However, training decision trees can be computationally expensive, particularly for large and high-dimensional datasets, largely due to the exhaustive search over candidate splits at each node. To improve computational efficiency, we propose Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors. By incorporating class-aware structure, DICS significantly reduces the split search space for classification tasks while preserving predictive performance. We further provide theoretical analysis showing that under the stated assumptions, DICS does not degrade the performance of classification trees compared to exhaustive split search. DICS can be incorporated into classification trees, random forests, and gradient-boosting models. Extensive experiments demonstrate that DICS achieves comparable accuracy while substantially reducing training time across synthetic and benchmark datasets, highlighting the benefit of integrating data-informed priors into split selection for scalable classification tree learning.
