Papers on “XGBoost”
Research on “XGBoost” in the 480 tracked journals: papers per year and category, the journals, institutions and countries publishing it, the keywords studied with it and its most-cited papers. It is not among the most frequent keywords of the last 30 days, so this page has no list of its latest papers. Also as Markdown.
← Top research keywords · Keyword trends
Key findings#
- From 2017 to 2026 (so far), the keyword “XGBoost” appeared in 665 papers in the tracked journals, most of them in AI (334), operations research (101) and finance (84).
- 187 of them were published in 2025, against 2 in 2017.
- Since 2023, the institutions with the most of these papers were Centre National de la Recherche Scientifique (7), King Fahd University of Petroleum and Minerals (6) and Norwegian University of Science and Technology (6).
- The journals publishing most of them were Expert Systems with Applications (105), Neural Computing and Applications (49) and Computational Economics (42).
- The most-cited of them is “A comparative analysis of gradient boosting algorithms” (Artificial Intelligence Review, 2020), with 2,995 citations.
Papers per year#
20172026 (so far)
| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---|---|---|---|---|---|---|---|---|---|
| AI | 1 | 3 | 8 | 11 | 26 | 30 | 52 | 55 | 96 | 52 |
| Operations Research | 0 | 1 | 2 | 1 | 8 | 7 | 13 | 20 | 27 | 22 |
| Finance | 0 | 1 | 0 | 1 | 3 | 4 | 11 | 15 | 28 | 21 |
| Economics | 0 | 0 | 0 | 2 | 3 | 7 | 4 | 14 | 22 | 30 |
| Information Systems | 1 | 1 | 0 | 1 | 0 | 2 | 1 | 7 | 4 | 5 |
| Management | 0 | 1 | 0 | 1 | 0 | 0 | 4 | 0 | 6 | 6 |
| Higher Education and Research | 0 | 0 | 0 | 0 | 0 | 3 | 1 | 3 | 1 | 1 |
| Statistics | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 2 |
| Marketing | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 |
| Tourism and Hospitality | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 |
| Health Economics and Policy | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 2 | 7 | 10 | 17 | 40 | 54 | 87 | 117 | 187 | 144 |
Where the papers come from#
| # | Journal | Papers |
|---|---|---|
| 1 | Expert Systems with Applications | 105 |
| 2 | Neural Computing and Applications | 49 |
| 3 | Computational Economics | 42 |
| 4 | Journal Of Big Data | 31 |
| 5 | Finance research letters | 18 |
| 6 | Computers & Industrial Engineering | 13 |
| 7 | Annals of Operations Research | 11 |
| 8 | Applied Intelligence | 11 |
| 9 | Knowledge-Based Systems | 11 |
| 10 | Knowledge and Information Systems | 9 |
| # | Institution | Papers |
|---|---|---|
| 1 | Centre National de la Recherche Scientifique | 7 |
| 2 | King Fahd University of Petroleum and Minerals | 6 |
| 3 | Norwegian University of Science and Technology | 6 |
| 4 | Tongji University | 6 |
| 5 | Beijing University of Chemical Technology | 5 |
| 6 | Central South University | 5 |
| 7 | Chinese Academy of Sciences | 5 |
| 8 | Harbin Institute of Technology | 5 |
| 9 | Kafrelsheikh University | 5 |
| 10 | Southeast University | 5 |
| # | Country | Papers |
|---|---|---|
| 1 | China | 155 |
| 2 | India | 46 |
| 3 | United States | 46 |
| 4 | Turkey | 39 |
| 5 | United Kingdom | 36 |
| 6 | Saudi Arabia | 28 |
| 7 | Australia | 24 |
| 8 | Spain | 22 |
| 9 | Iran | 21 |
| 10 | Taiwan | 19 |
| # | Keyword | Papers |
|---|---|---|
| 1 | random forest | 149 |
| 2 | SHAP | 108 |
| 3 | LightGBM | 58 |
| 4 | ensemble learning | 54 |
| 5 | class imbalance | 53 |
| 6 | feature selection | 52 |
| 7 | CatBoost | 44 |
| 8 | LSTM | 40 |
| 9 | feature importance | 40 |
Most-cited papers#
| # | Citations | Per month | Title | Journal | Published | Authors |
|---|---|---|---|---|---|---|
| 1 | 2,995 | 40.3 | A comparative analysis of gradient boosting algorithms | Artificial Intelligence Review | 2020-08-24 | Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz |
| 2 | 2,181 | 36.8 | Tabular data: Deep learning is not all you need | Information Fusion | 2021-11-26 | Ravid Shwartz-Ziv, Amitai Armon |
| 3 | 770 | 6.6 | A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring | Expert Systems with Applications | 2017-02-10 | Yufei Xia, Chuanzhe Liu, Yuying Li et al. |
| 4 | 633 | 20.2 | Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods | Journal Of Big Data | 2024-03-26 | Huanjing Wang, Qianxin Liang, John Hancock et al. |
| 5 | 588 | 7.8 | A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities | Information Fusion | 2020-07-27 | Sergio González, Salvador García, Javier Del Ser et al. |
Cite this page
Scholar4AI. (2026). Papers on “XGBoost”. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/keywords/xgboost
@misc{scholar4ai-keywords-xgboost,
author = {{Scholar4AI}},
title = {Papers on “XGBoost”},
year = {2026},
howpublished = {\url{https://scholar4ai.com/keywords/xgboost}},
urldate = {2026-11-03},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}