Comparar métodos

	Árvore de Decisão Auto-supervisionada	Propagação de Rótulos
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2015–present	2002
Autor original	Multiple authors (active research area, 2010s–2020s)	Zhu, X. & Ghahramani, Z.
Tipo	Self-supervised ensemble/single tree model	Graph-based semi-supervised classification
Fonte seminal	Self-supervised learning. Wikipedia.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Outros nomes	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relacionados	5	3
Resumo	Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.	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.