Comparer des méthodes

	Ensemble Naive Bayes	Bayes naïf semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s	2000
Auteur d'origine	Various (Dietterich, T.G.; Webb, G.I.; others)	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Type	Ensemble of probabilistic classifiers	Semi-supervised generative classifier
Source fondatrice	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.	Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134.
Alias	Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble	SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier
Apparentées	6	4
Résumé	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.	Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.