Multi-Layered Gradient Boosting Decision Trees

TL;DR

This paper introduces multi-layered GBDT forests (mGBDTs), enabling hierarchical representation learning in non-differentiable models through target propagation, demonstrating improved performance and interpretability.

Contribution

It proposes a novel multi-layered GBDT architecture that learns hierarchical features without backpropagation, expanding GBDT capabilities.

Findings

Effective hierarchical representation learning demonstrated.

Improved performance over traditional GBDTs.

Visualizations confirm learned feature hierarchies.

Abstract

Multi-layered representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision. While non-differentiable models such as gradient boosting decision trees (GBDTs) are the dominant methods for modeling discrete or tabular data, they are hard to incorporate with such representation learning ability. In this work, we propose the multi-layered GBDT forest (mGBDTs), with an explicit emphasis on exploring the ability to learn hierarchical representations by stacking several layers of regression GBDTs as its building block. The model can be jointly trained by a variant of target propagation across layers, without the need to derive back-propagation nor differentiability. Experiments and visualizations confirmed the effectiveness of the model in terms of performance and representation learning ability.