Affiliations: [a] College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou 550025, China | [b] Guizhou Engineer Lab of ACMIS, Guizhou University, Guiyang, Guizhou 550025, China | [c] National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
Correspondence:
[*]
Corresponding author: Hui Li, College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou 550025, China. E-mail: cse.HuiLi@gzu.edu.cn.
Abstract: Gradient Boosting Decision Tree (GBDT) has been used extensively in machine learning applications due to its superiority in efficiency, accuracy and interpretability. Although there are already excellent and popular open source implementations such as XGBoost and LightGBM, etc., however, large data size tend to make scalable and efficient learning to be very difficult. Since sampling is an efficient technique for alleviate massive data analysis performance issues, we exploit sampling techniques to address this problem. In this paper, we propose the AdaGBDT approach which apply an adaptive sampling method based on Massart’s Inequality to build GBDT model and draws samples in an on-line manner without manually specifying sample size. AdaGBDT is implemented by integrating the adaptive sampling method into LightGBM. The experimental results showed that, AdaGBDT not only keeps a small sample size and has a better training performance than LightGBM, but also subject to the constraint of estimation accuracy and confidence.
