Method for Improving Gradient Boosting Learning Efficiency Based on Modified Loss Functions

被引：0

作者：

Korolev, N. S. ^{[1
]}

Senko, O. V. ^{[2
]}

机构：

[1] Lomonosov Moscow State Univ, Moscow 119991, Russia

[2] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow 119333, Russia

来源：

AUTOMATION AND REMOTE CONTROL | 2022年 / 83卷 / 12期

关键词：

gradient boosting; decision tree; loss function; machine learning; data analysis;

D O I：

10.1134/S00051179220120074

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We consider a new method to improve the quality of training in gradient boosting as wellas to increase its generalization performance based on the use of modified loss functions. Incomputational experiments, the possible applicability of this method to improve the quality ofgradient boosting when solving various classification and regression problems on real data isshown.

引用

页码：1935 / 1943

页数：9

共 50 条

[31] Link quality estimation method based on gradient boosting decision tree
Zhang, Yan
Shu, Jian
INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2021, 36 (03) : 159 - 166
[32] Traffic Flow Forecasting Method based on Gradient Boosting Decision Tree
Xia Ying
Chen Jungang
PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON FRONTIERS OF MANUFACTURING SCIENCE AND MEASURING TECHNOLOGY (FMSMT 2017), 2017, 130 : 413 - 416
[33] WLAN indoor positioning method based on gradient boosting and particle filtering
Hu L.
Li Z.
Yang X.
Wei C.
International Journal of Simulation and Process Modelling, 2019, 14 (06): : 535 - 545
[34] A Satellite Fault Diagnosis and Analysis Method based on Extreme Gradient Boosting
Liu, Xiaopeng
Wang, Yuechuan
Chai, Senchun
Li, Zhaoyang
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 6694 - 6699
[35] A modified scaled conjugate gradient method with global convergence for nonconvex functions
Babaie-Kafaki, Saman
Ghanbari, Reza
BULLETIN OF THE BELGIAN MATHEMATICAL SOCIETY-SIMON STEVIN, 2014, 21 (03) : 465 - 477
[36] Improving the efficiency of counting defects by learning RBF nets with MAD loss
Rafajlowicz, Ewaryst
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2008, PROCEEDINGS, 2008, 5097 : 146 - 153
[37] Ensembling Learning Based Melanoma Classification Using Gradient Boosting Decision Trees
Han, Yipeng
Zheng, Xiaolu
AIPR 2020: 2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND PATTERN RECOGNITION, 2020, : 104 - 109
[38] Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting
Gautheron, Leo
Germain, Pascal
Habrard, Amaury
Metzler, Guillaume
Morvant, Emilie
Sebban, Marc
Zantedeschi, Valentina
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT III, 2021, 12459 : 141 - 157
[39] An adjustable machine learning gradient boosting-based controller for PV applications
Omer, Zahi M.
Shareef, Hussain
INTELLIGENT SYSTEMS WITH APPLICATIONS, 2023, 19
[40] Road surface temperature prediction based on gradient extreme learning machine boosting
Liu, Bo
Yan, Shuo
You, Huanling
Dong, Yan
Li, Yong
Lang, Jianlei
Gu, Rentao
COMPUTERS IN INDUSTRY, 2018, 99 : 294 - 302

← 1 2 3 4 5 →