Gradient Boosting Neural Networks: GrowNet
Machine Learning
2020-06-16 v2 Machine Learning
Abstract
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank. A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model components and model hyperparameters.
Cite
@article{arxiv.2002.07971,
title = {Gradient Boosting Neural Networks: GrowNet},
author = {Sarkhan Badirli and Xuanqing Liu and Zhengming Xing and Avradeep Bhowmik and Khoa Doan and Sathiya S. Keerthi},
journal= {arXiv preprint arXiv:2002.07971},
year = {2020}
}
Comments
Supplementary material starts after references