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Light Gradient Boosting Machine (LGBM)
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LGBM is a gradient boosting framework using tree-based learning algorithms [16]. In LGBM, gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) were the 2 main techniques to improve efficiency and scalability. GOSS kept those data with large gradients and randomly dropped those with small gradients and reduced the calculation cost. EFB bundled exclusive features to reduce feature dimensions. The feature bundles could improve training efficiency without losing accuracy.
Copyright and License information: ©2022 Po-Yuan Su, Yi-Chia Wei, Hao Luo, Chi-Hung Liu, Wen-Yi Huang, Kuan-Fu Chen, Ching-Po Lin, Hung-Yu Wei, Tsong-Hai Lee. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 25.03.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
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