Сравнение методов

	Рассуждение на основе прецедентов (CBR)	Дерево решений	Нечеткие когнитивные карты (FCM)
Область	Мягкие вычисления	Машинное обучение	Мягкие вычисления
Семейство	Machine learning	Machine learning	Process / pipeline
Год появления	1994	1984	1986
Автор метода	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)	Breiman, Friedman, Olshen & Stone	Bart Kosko
Тип	Experience-based (analogical) problem solving	Recursive partitioning (if-then rules)	Fuzzy causal/feedback network for scenario analysis
Основополагающий источник	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.
Другие названия	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar
Связанные	2	5	4
Сводка	Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.