Linganisha mbinu

	Sheria za Chama cha Ensemble	Bagging (Bootstrap Aggregating)	Kuimarisha
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	late 1990s–2000s	1996	1990–1997
Mwanzilishi	Various (applied ensemble philosophy from Breiman and others to association rule mining)	Breiman, L.	Schapire, R. E.; Freund, Y.
Aina	Ensemble meta-learning over association rule learners	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	5	6
Muhtasari	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.