Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Semi-supervised XGBoost× | Label Propagation× | XGBoost× | |
|---|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 2016–2018 | 2002 | 2016 |
| Ophavsperson≠ | Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors | Zhu, X. & Ghahramani, Z. | Chen, T. & Guestrin, C. |
| Type≠ | Ensemble (semi-supervised gradient boosting) | Graph-based semi-supervised classification | Ensemble (gradient-boosted decision trees) |
| Oprindelig kilde≠ | 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 ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Aliasser≠ | SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relaterede≠ | 4 | 3 | 5 |
| Resumé≠ | 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. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. | 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. |
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