Methoden vergleichen

	Dempster-Shafer-Theorie der Evidenz	Granulares Rechnen (Informationsgranulation)
Fachgebiet	Soft Computing	Soft Computing
Familie	Machine learning	Machine learning
Entstehungsjahr	1976	1997
Urheber	Arthur P. Dempster & Glenn Shafer	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao
Typ	Uncertainty calculus for combining evidence	Framework for multi-granularity information processing
Wegweisende Quelle	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.
Aliasnamen	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama
Verwandt	4	3
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.	Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.