Tree ensemble algorithms as RandomForest and GradientBoosting are currently
the dominant methods for modeling discrete or tabular data, however, they are
unable to perform a hierarchical representation learning from raw data as
NeuralNetworks does thanks to its multi-layered structure, which is a key
feature for DeepLearning problems and modeling unstructured data. This
limitation is due to the fact that tree algorithms can not be trained with
back-propagation because of their mathematical nature. However, in this work,
we demonstrate that the mathematical formulation of bagging and boosting can be
combined together to define a graph-structured-tree-ensemble algorithm with a
distributed representation learning process between trees naturally (without
using back-propagation). We call this novel approach Distributed Gradient
Boosting Forest (DGBF) and we demonstrate that both RandomForest and
GradientBoosting can be expressed as particular graph architectures of DGBT.
Finally, we see that the distributed learning outperforms both RandomForest and
GradientBoosting in 7 out of 9 datasets.

决策树组合算法（如 RandomForest 和 GradientBoosting）在对于离散或表格数据进行建模方面是主导方法，但是由于它们无法像神经网络那样从原始数据中进行层次化表示学习，因此限制了其在深度学习问题和建模非结构化数据方面的应用。然而，本研究表明通过将 bagging 和 boosting 的数学形式结合起来，可以定义一个具有分布式表示学习过程的图结构树集成算法，并且不需要使用反向传播算法，我们称之为分布式梯度提升森林（DGBF）。最后，我们发现分布式学习在 9 个数据集中的 7 个中表现优于 RandomForest 和 GradientBoosting。