RT info:eu-repo/semantics/article
T1 A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF)
A1 Delgado Panadero, Ángel
A1 Benítez Andrades, José Alberto
A1 García Ordás, María Teresa
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Ingenierías
K1 Ingeniería de sistemas
K1 CART
K1 GBDT
K1 Ensemble
K1 Representation learning
K1 Distributed learning
K1 3306 Ingeniería y Tecnología Eléctricas
K1 33 Ciencias Tecnológicas
AB [EN] 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.
PB Springer
SN 0924-669X
LK https://hdl.handle.net/10612/18165
UL https://hdl.handle.net/10612/18165
NO Delgado-Panadero, Á., Benítez-Andrades, J. A., & García-Ordás, M. T. (2023). A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF). Applied Intelligence, 53(19), 22991-23003. https://doi.org/10.1007/S10489-023-04735-W
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 21-may-2024