方法对比

	证据的Dempster-Shafer理论	粒计算（信息粒化）
领域	软计算	软计算
方法族	Machine learning	Machine learning
起源年份	1976	1997
提出者	Arthur P. Dempster & Glenn Shafer	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao
类型	Uncertainty calculus for combining evidence	Framework for multi-granularity information processing
开创性文献	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.
别名	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama
相关	4	3
摘要	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.