arXiv AI
3d ago

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

The paper introduces a moving-horizon approximate branch‑and‑reduce method for training deep classification trees on large datasets with continuous features. It combines a hierarchical root‑subtree optimization framework, branch‑and‑reduce at the root, greedy heuristics for subtrees, and a low‑cost moving‑horizon refinement to improve accuracy. Experiments show the approach surpasses heuristic baselines in test accuracy while scaling better in dataset size and tree depth than existing global optimal solvers.

By Chenxuanyin Zou, Jiayang Ren, Qiangqiang Mao, Jing Liu, Marcus Lai, Yankai Cao
arXiv Machine Learning
Jul 3

Conditional Inference Trees and Forests for Feature Selection

arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.

By Robert Milletich, Justin Downes, Steve Goley, Newel Hirst
arXiv Machine Learning
Aug 6

ArborEnum: Decision Tree Rashomon Sets over Continuous Features

arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.

By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin