A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees

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.