Development and Application of Light Gradient Boosting Machine

被引：0

作者：

Wei, Jiamei ^{[1
]}

Yuan, Shujuan ^{[1
]}

Kong, Shanshan ^{[1
]}

Yang, Aimin ^{[1
,2
,3
,4
,5
]}

Zhao, Chenying ^{[1
]}

机构：

[1] College of Science, North China University of Science and Technology, Hebei, Tangshan

[2] Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Hebei, Tangshan

[3] Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Hebei, Tangshan

[4] The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Hebei, Tangshan

[5] Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Hebei, Tangshan

来源：

Computer Engineering and Applications | 2025年 / 61卷 / 05期

关键词：

decision tree; ensemble learning; light gradient boosting machine (LightGBM); machine learning;

D O I：

10.3778/j.issn.1002-8331.2405-0396

中图分类号：

学科分类号：

摘要：

Light gradient boosting machine (LightGBM) is one of the more powerful algorithms in the field of machine learning. LightGBM uses an efficient tree learning algorithm to train models faster. Its unique histogram bucketing method and gradient-based one-sided leaf growing technique reduce memory usage and computational cost. LightGBM is widely used in medical, natural language processing, finance, industrial manufacturing and other fields. However, LightGBM still faces many challenges in high-dimensional data processing, category feature processing, and model interpretability, etc. At present, the methods to solve these problems mainly focus on feature engineering, visualization, model mixing, etc, and have achieved good results. Firstly, the algorithm principles and variants of the decision tree family are introduced. Secondly, the principles, advantages and disadvantages of LightGBM are sorted out, the challenges faced by the algorithm are summarized, and the future research hot spots and difficulties of LightGBM are pointed out. Finally, the development of LightGBM is summarized and prospected. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：32 / 42

页数：10

共 60 条

[1]

LUO C W, WANG S S, YIN J S, Et al., Research status and prospect of ensemble learning, Journal of Command and Control, 9, 1, pp. 1-8, (2023)

[2]

DU G S, LIU Z X, LU H F., Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment, Journal of Computational and Applied Mathematics, 386, (2021)

[3]

YE Y G, SHI J S, ZHU D X, Et al., Management of medical and health big data based on integrated learning-based health care system: a review and comparative analysis, Computer Methods and Programs in Biomedicine, 209, (2021)

[4]

ZHONG X R, DUAN S K, WANG L D., An effective and efficient broad-based ensemble learning model for moderate-large scale image recognition, Artificial Intelligence Review, 56, 5, pp. 4197-4215, (2023)

[5]

BAI S J, BAI J, SI Q L, Et al., Deep ensemble learning for diversified 3D model classification, Computer Engineering and Applications, 59, 5, pp. 222-231, (2023)

[6]

GU Q H, SUN W J, LI X X, Et al., A new ensemble classification approach based on rotation forest and lightGBM, Neural Computing and Applications, 35, 15, pp. 11287-11308, (2023)

[7]

MCTAVISH H, ZHONG C D, ACHERMANN R, Et al., Fast sparse decision tree optimization via reference ensembles, Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9604-9613, (2022)

[8]

PHAM B T, PHONG T V, NGUYEN-THOI T, Et al., Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers, Geocarto International, 37, 3, pp. 735-757, (2022)

[9]

FAN S B, ZHANG Z J, HUANG J., Association rule classification method strengthened by decision tree pruning, Computer Engineering and Applications, 59, 5, pp. 87-94, (2023)

[10]

QUINLAN J R., Induction of decision trees, Machine Learning, 1, 1, pp. 81-106, (1986)

← 1 2 3 4 5 6 →