Methoden vergleichen

	Dempster-Shafer-Theorie der Evidenz	Regelinduktion (RIPPER)
Fachgebiet	Soft Computing	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1976	1995
Urheber	Arthur P. Dempster & Glenn Shafer	William W. Cohen
Typ	Uncertainty calculus for combining evidence	Supervised rule learning algorithm
Wegweisende Quelle	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Cohen, W. W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123.
Aliasnamen	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	RIPPER, Propositional Rule Learning, Kural Tümevarımı, Inductive Rule Learning
Verwandt	4	2
Zusammenfassung	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.	Rule Induction, and specifically the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) algorithm, is a supervised machine learning method that learns a compact set of IF-THEN classification rules from labeled training data. Introduced by William W. Cohen in 1995, RIPPER applies a separate-and-conquer strategy combined with minimum description length (MDL) pruning to generate rules that are both accurate and interpretable, making it a landmark algorithm in the field of inductive rule learning.