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cs.LG2025
Convolutional Model Trees
William Ward Armstrong, Hongyi Li, Jun Xu
A method for creating a forest of model trees to fit samples of a function defined on images is described in several steps: down-sampling the images, determining a tree's hyperplan…
cs.LG2025
LHT: Statistically-Driven Oblique Decision Trees for Interpretable Classification
Hongyi Li, Jun Xu, William Ward Armstrong
We introduce the Learning Hyperplane Tree (LHT), a novel oblique decision tree model designed for expressive and interpretable classification. LHT fundamentally distinguishes itsel…
cs.LG2025
Learning Hyperplane Tree: A Piecewise Linear and Fully Interpretable Decision-making Framework
Hongyi Li, Jun Xu, William Ward Armstrong
This paper introduces a novel tree-based model, Learning Hyperplane Tree (LHT), which outperforms state-of-the-art (SOTA) tree models for classification tasks on several public dat…