4 papers
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…
Exact Learning of Linear Model Predictive Control Laws using Oblique Decision Trees with Linear Predictions
Jiayang Ren, Qiangqiang Mao, Tianwei Zhao +1
Model Predictive Control (MPC) is a powerful strategy for constrained multivariable systems but faces computational challenges in real-time deployment due to its online optimizatio…
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…
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…