Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Causale Ontdekking Algoritmen (PC, FCI, LiNGAM)× | Graph Attention Network× | |
|---|---|---|
| Vakgebied≠ | Causale inferentie | Deep learning |
| Familie≠ | Regression model | Machine learning |
| Jaar van ontstaan≠ | 2000 | 2018 |
| Grondlegger≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Veličković, P. et al. |
| Type≠ | Causal structure learning | Graph neural network (attention-based) |
| Oorspronkelijke bron≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Aliassen≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). |
| ScholarGateGegevensset ↗ |
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