Papers on “random forest”
Research on “random forest” 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.
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Key findings#
- From 2017 to 2026 (so far), the keyword “random forest” appeared in 1,440 papers in the tracked journals, most of them in AI (711), operations research (221) and economics (166).
- 228 of them were published in 2025, against 52 in 2017.
- Since 2023, the institutions with the most of these papers were Centre National de la Recherche Scientifique (15), Kafrelsheikh University (10) and King Fahd University of Petroleum and Minerals (8).
- The journals publishing most of them were Expert Systems with Applications (109), Neural Computing and Applications (82) and Computational Economics (41).
- 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 | 35 | 53 | 57 | 52 | 69 | 113 | 95 | 87 | 91 | 59 |
| Operations Research | 6 | 6 | 14 | 13 | 12 | 35 | 28 | 37 | 40 | 30 |
| Economics | 3 | 4 | 6 | 17 | 18 | 11 | 17 | 22 | 36 | 32 |
| Finance | 3 | 2 | 3 | 7 | 13 | 12 | 12 | 23 | 36 | 21 |
| Statistics | 2 | 2 | 6 | 3 | 3 | 9 | 4 | 12 | 5 | 14 |
| Information Systems | 2 | 5 | 3 | 6 | 4 | 3 | 4 | 9 | 9 | 7 |
| Management | 1 | 1 | 2 | 3 | 7 | 2 | 9 | 6 | 7 | 8 |
| Higher Education and Research | 0 | 4 | 1 | 0 | 6 | 9 | 2 | 4 | 0 | 1 |
| Marketing | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 5 | 1 | 1 |
| Accounting | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 2 | 0 |
| Health Economics and Policy | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 |
| Tourism and Hospitality | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| All categories | 52 | 79 | 92 | 102 | 135 | 198 | 173 | 208 | 228 | 173 |
Where the papers come from#
| # | Journal | Papers |
|---|---|---|
| 1 | Expert Systems with Applications | 109 |
| 2 | Neural Computing and Applications | 82 |
| 3 | Computational Economics | 41 |
| 4 | Journal Of Big Data | 26 |
| 5 | Annals of Operations Research | 24 |
| 6 | Computers & Industrial Engineering | 24 |
| 7 | Knowledge-Based Systems | 18 |
| 8 | Finance research letters | 18 |
| 9 | Decision Analytics Journal | 18 |
| 10 | Artificial Intelligence Review | 17 |
| # | Institution | Papers |
|---|---|---|
| 1 | Centre National de la Recherche Scientifique | 15 |
| 2 | Kafrelsheikh University | 10 |
| 3 | King Fahd University of Petroleum and Minerals | 8 |
| 4 | Universiti Sains Malaysia | 8 |
| 5 | Central South University | 6 |
| 6 | Huazhong University of Science and Technology | 6 |
| 7 | Lebanese American University | 6 |
| 8 | Mansoura University | 6 |
| 9 | New Mansoura University | 6 |
| 10 | University of Twente | 6 |
| # | Country | Papers |
|---|---|---|
| 1 | China | 163 |
| 2 | United States | 91 |
| 3 | United Kingdom | 68 |
| 4 | Turkey | 66 |
| 5 | India | 63 |
| 6 | Spain | 42 |
| 7 | Germany | 41 |
| 8 | Italy | 41 |
| 9 | France | 38 |
| 10 | Saudi Arabia | 33 |
| # | Keyword | Papers |
|---|---|---|
| 1 | XGBoost | 149 |
| 2 | support vector machine | 99 |
| 3 | artificial neural networks | 70 |
| 4 | feature selection | 67 |
| 5 | SHAP | 65 |
| 6 | decision tree | 64 |
| 7 | ensemble learning | 63 |
| 8 | gradient boosting | 62 |
| 9 | class imbalance | 61 |
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 | 2,828 | 26.4 | Deep learning with long short-term memory networks for financial market predictions | European Journal of Operational Research | 2017-12-05 | Thomas Günter Fischer, Christopher Krauß |
| 3 | 1,561 | 17.5 | A comparison of random forest variable selection methods for classification prediction modeling | Expert Systems with Applications | 2019-05-23 | Jaime Lynn Speiser, Michael E. Miller, Janet Austin Tooze et al. |
| 4 | 1,068 | 17.7 | A survey on missing data in machine learning | Journal Of Big Data | 2021-10-27 | Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng et al. |
| 5 | 983 | 13.0 | Selecting critical features for data classification based on machine learning methods | Journal Of Big Data | 2020-07-23 | Rung-Ching Chen, Christine Dewi, Su-Wen Huang et al. |
Cite this page
Scholar4AI. (2026). Papers on “random forest”. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/keywords/random-forest
@misc{scholar4ai-keywords-random-forest,
author = {{Scholar4AI}},
title = {Papers on “random forest”},
year = {2026},
howpublished = {\url{https://scholar4ai.com/keywords/random-forest}},
urldate = {2026-11-03},
note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}