LightGBM: A Highly Efficient Gradient Boosting Decision Tree
- Journal
- HAL (Le Centre pour la Communication Scientifique Directe)
In The Last Decade
doi.org/w89097928 →Countries where authors are citing LightGBM: A Highly Efficient Gradient Boosting Decision Tree
This map shows the geographic impact of LightGBM: A Highly Efficient Gradient Boosting Decision Tree. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by LightGBM: A Highly Efficient Gradient Boosting Decision Tree with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites LightGBM: A Highly Efficient Gradient Boosting Decision Tree more than expected).
Fields of papers citing LightGBM: A Highly Efficient Gradient Boosting Decision Tree
This network shows the impact of LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the LightGBM: A Highly Efficient Gradient Boosting Decision Tree.
About LightGBM: A Highly Efficient Gradient Boosting Decision Tree
This paper, published in 2017, received 6.5k indexed citations . Written by Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye and Tie‐Yan Liu covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (1.8k citations), Electrical and Electronic Engineering (701 citations) and Environmental Engineering (596 citations). Published in HAL (Le Centre pour la Communication Scientifique Directe).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w89097928.