Comparer des méthodes

	Théorie de Dempster-Shafer des évidences	Naive Bayes
Domaine	Soft computing	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1976	1997
Auteur d'origine	Arthur P. Dempster & Glenn Shafer	Mitchell, T. M. (textbook treatment)
Type	Uncertainty calculus for combining evidence	Probabilistic classifier (Bayes' theorem with conditional independence)
Source fondatrice	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Alias	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Apparentées	4	4
Résumé	Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.