Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Forêt aléatoire auto-supervisée× | XGBoost× | |
|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2012–2022 | 2016 |
| Auteur d'origine≠ | Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage) | Chen, T. & Guestrin, C. |
| Type≠ | Semi-supervised ensemble (self-supervised pretext task + RF) | Ensemble (gradient-boosted decision trees) |
| Source fondatrice≠ | Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling | XGBoost, extreme gradient boosting, scalable tree boosting |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateJeu de données ↗ |
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