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| 基于案例推理 (CBR)× | 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≠ | 1994 | 1997 |
| 提出者≠ | Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle) | Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao |
| 类型≠ | Experience-based (analogical) problem solving | Framework for multi-granularity information processing |
| 开创性文献≠ | Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59. DOI ↗ | 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. DOI ↗ |
| 别名 | CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme | information granulation, computing with granules, three-way granular computing, tanecikli hesaplama |
| 相关≠ | 2 | 3 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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