Krahasoni metodat

	Rregullat e asociacionit në formë ansambli	Bagim (Agregimi Bootstrap)	Boosting
Fusha	Mësimi i makinës	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning	Machine learning
Viti i origjinës	late 1990s–2000s	1996	1990–1997
Krijuesi	Various (applied ensemble philosophy from Breiman and others to association rule mining)	Breiman, L.	Schapire, R. E.; Freund, Y.
Lloji	Ensemble meta-learning over association rule learners	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)
Burimi themelues	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	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.
Emërtime të tjera	Ensemble ARM, aggregated association rules, combined frequent-pattern mining, multi-run association rule learning	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Të lidhura	6	5	6
Përmbledhja	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.