DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers

2026-08-20

AI Summary

本文提出DICS方法，通过聚类构建候选分裂点集合以减少决策树训练中的计算成本，同时保持预测性能。

Abstract

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.

Problem

• decision tree
• computational efficiency
• candidate splits
• large and high-dimensional datasets

Innovation

• Data-Informed Centroid Splitting
• computational efficiency
• candidate splits
• class-aware structure
• classification tasks