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GBDT-MO: Gradient-Boosted Decision Trees for Multiple Outputs.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Jul2021, Vol. 32 Issue 7, p3156-3167. 12p. - Publication Year :
- 2021
-
Abstract
- Gradient-boosted decision trees (GBDTs) are widely used in machine learning, and the output of current GBDT implementations is a single variable. When there are multiple outputs, GBDT constructs multiple trees corresponding to the output variables. The correlations between variables are ignored by such a strategy causing redundancy of the learned tree structures. In this article, we propose a general method to learn GBDT for multiple outputs, called GBDT-MO. Each leaf of GBDT-MO constructs predictions of all variables or a subset of automatically selected variables. This is achieved by considering the summation of objective gains over all output variables. Moreover, we extend histogram approximation into the multiple-output case to speed up training. Various experiments on synthetic and real data sets verify that GBDT-MO achieves outstanding performance in terms of accuracy, training speed, and inference speed. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DECISION trees
*MIMO systems
*MACHINE learning
*APPROXIMATION algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 32
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 151306537
- Full Text :
- https://doi.org/10.1109/TNNLS.2020.3009776