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| Halbüberwachte LightGBM× | LightGBM× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2017–2019 | 2017 |
| Urheber≠ | Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research | Ke, G. et al. (Microsoft) |
| Typ≠ | Semi-supervised gradient boosting ensemble | Gradient boosting decision tree ensemble |
| Wegweisende Quelle≠ | 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 ↗ | 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 (NeurIPS) 30, 3146–3154. link ↗ |
| Aliasnamen | SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. |
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