Comparar métodos

	Árbol de Decisión Semisupervisado	Propagación de Etiquetas
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2000s	2002
Autor original	Various (Levin & Shapiro; Zhu & Goldberg lineage)	Zhu, X. & Ghahramani, Z.
Tipo	Semi-supervised classifier / regressor	Graph-based semi-supervised classification
Fuente seminal	Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Alias	SSDT, semi-supervised tree induction, self-training decision tree, label-propagation tree	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relacionados	4	3
Resumen	A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.	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.