Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Algoritmo Ensemble Apriori× | Boosting× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1994 (Apriori base); ensemble extensions 2000s–2010s | 1990–1997 |
| Autor original≠ | Agrawal, R. & Srikant, R. (Apriori base); ensemble extension by multiple researchers | Schapire, R. E.; Freund, Y. |
| Tipo≠ | Ensemble / Frequent Pattern Mining | Sequential ensemble (iterative reweighting) |
| Fonte seminal≠ | Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499. link ↗ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ |
| Outros nomes | Ensemble Apriori, Ensemble Association Rule Mining, EAR mining, Distributed Apriori Ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | The Ensemble Apriori Algorithm applies ensemble principles to the classic Apriori frequent-pattern miner by running multiple Apriori instances on different data partitions or parameter settings and merging their rule sets. This approach improves coverage, reduces sensitivity to the minimum-support threshold, and scales association rule mining to larger transactional datasets. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
| ScholarGateConjunto de dados ↗ |
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