方法对比

	集成朴素贝叶斯	Bagging（Bootstrap Aggregating）	Boosting
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2000s	1996	1990–1997
提出者	Various (Dietterich, T.G.; Webb, G.I.; others)	Breiman, L.	Schapire, R. E.; Freund, Y.
类型	Ensemble of probabilistic classifiers	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)
开创性文献	Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer.	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.
别名	Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
相关	6	5	6
摘要	Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes models while reducing variance and improving accuracy through ensemble aggregation.	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.