Сравнение на методи

	Теория на доказателствата на Демпстър-Шафър	Фази когнитивни карти (FCM)	Индукция на правила (RIPPER)
Област	Меки изчисления	Меки изчисления	Машинно обучение
Семейство	Machine learning	Process / pipeline	Machine learning
Година на възникване	1976	1986	1995
Създател	Arthur P. Dempster & Glenn Shafer	Bart Kosko	William W. Cohen
Тип	Uncertainty calculus for combining evidence	Fuzzy causal/feedback network for scenario analysis	Supervised rule learning algorithm
Основополагащ източник	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.	Cohen, W. W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123.
Други названия	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar	RIPPER, Propositional Rule Learning, Kural Tümevarımı, Inductive Rule Learning
Свързани	4	4	2
Резюме	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.	A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.	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.