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cs.LG2026
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
Chenxuanyin Zou, Jiayang Ren, Qiangqiang Mao +3
Despite the importance for interpretability, decision trees face severe scalability challenges. Existing global optimal methods are often limited by binary feature selection and sh…
cs.LG2024
Can a Single Tree Outperform an Entire Forest?
Qiangqiang Mao, Yankai Cao
The prevailing mindset is that a single decision tree underperforms classic random forests in testing accuracy, despite its advantages in interpretability and lightweight structure…
cs.LG2023
A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets
Jiayang Ren, Valentín Osuna-Enciso, Morimasa Okamoto +5
Decision trees are essential yet NP-complete to train, prompting the widespread use of heuristic methods such as CART, which suffers from sub-optimal performance due to its greedy…