方法对比

	半监督XGBoost	标签传播	随机森林
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2016–2018	2002	2001
提出者	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors	Zhu, X. & Ghahramani, Z.	Breiman, L.
类型	Ensemble (semi-supervised gradient boosting)	Graph-based semi-supervised classification	Ensemble (bagging of decision trees)
开创性文献	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
别名	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	4	3	4
摘要	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.