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| 준지도학습 LightGBM× | 준지도 학습 XGBoost× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2019 | 2016–2018 |
| 창시자≠ | Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors |
| 유형≠ | Semi-supervised gradient boosting ensemble | Ensemble (semi-supervised gradient boosting) |
| 원전≠ | 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 ↗ |
| 별칭 | SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost |
| 관련 | 4 | 4 |
| 요약≠ | 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. |
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