Сравнение методов

	Трехуровневые решения	Рассуждение на основе прецедентов (CBR)
Область	Мягкие вычисления	Мягкие вычисления
Семейство	Machine learning	Machine learning
Год появления	2010	1994
Автор метода	Yiyu Yao	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)
Тип	Decision-theoretic classification framework	Experience-based (analogical) problem solving
Основополагающий источник	Yao, Y. (2010). Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353.	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.
Другие названия	3WD, Trisecting-and-Acting, Tri-partition Decision Making, Üç Yönlü Kararlar	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Связанные	2	2
Сводка	Three-Way Decisions (3WD) is a decision-theoretic framework, introduced by Yiyu Yao in 2010, that partitions the universe of objects into three regions—positive (accept), negative (reject), and boundary (abstain)—using probabilistic rough set theory. Unlike binary classifiers that force every object into one of two classes, 3WD explicitly acknowledges uncertainty by allowing a third option: deferring judgment when available evidence is insufficient for a confident decision.	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.