方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 半监督 LightGBM× | 半监督XGBoost× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–2019 | 2016–2018 |
| 提出者≠ | Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors |
| 类型≠ | Semi-supervised gradient boosting ensemble | Ensemble (semi-supervised gradient boosting) |
| 开创性文献≠ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ |
| 别名 | SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost |
| 相关 | 4 | 4 |
| 摘要≠ | Semi-supervised LightGBM combines LightGBM's highly efficient gradient boosting framework with semi-supervised strategies — most commonly pseudo-labeling or self-training — to exploit large pools of unlabeled data alongside a smaller labeled set, improving predictive performance when obtaining labels is costly or time-consuming. | Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce. |
| ScholarGate数据集 ↗ |
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