方法对比
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| 半监督 LightGBM× | 半监督随机森林× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–2019 | 2009 |
| 提出者≠ | Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| 类型≠ | Semi-supervised gradient boosting ensemble | Semi-supervised ensemble classifier |
| 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 ↗ | Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE. DOI ↗ |
| 别名 | SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| 相关≠ | 4 | 3 |
| 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 Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation. |
| ScholarGate数据集 ↗ |
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