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	Ensemble-associationsregler	Boosting
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	late 1990s–2000s	1990–1997
Upphovsperson	Various (applied ensemble philosophy from Breiman and others to association rule mining)	Schapire, R. E.; Freund, Y.
Typ	Ensemble meta-learning over association rule learners	Sequential ensemble (iterative reweighting)
Ursprungskälla	Domingos, P. (1999). MetaCost: A general method for making classifiers cost-sensitive. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 155–164.	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.
Alias	Ensemble ARM, aggregated association rules, combined frequent-pattern mining, multi-run association rule learning	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Närliggande	6	6
Sammanfattning	Ensemble Association Rules applies ensemble learning principles to association rule mining: multiple rule sets are discovered from different data subsamples or with varied parameters, then merged and weighted to produce a more stable and complete set of co-occurrence patterns. The approach reduces sensitivity to support and confidence threshold choices and improves robustness on noisy transactional data.	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.