Case-Based Reasoning Algorithm Based on Qualitative Causality

Zhile Liu, Lingfei Fu, Yanchun Zhou
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引用次数: 2

Abstract

With the limitation of low predictive performance and poor reliability in current case-based reasoning method, this paper proposes a case-based reasoning algorithm based on qualitative causality (CBR-QC). On the basis of traditional quantitative retrieval method, it introduces relevant knowledge about qualitative reasoning, describes the case qualitatively with qualitative method, constructs qualitative equations, and then gets case groups with requirements through qualitative retrieval. Euclidean distance similarity computation method and KNN method are applicable in this article to retrieve quantitatively similar cases from retrieved qualitatively similar cases, finally get prediction values through adapting the retrieved cases.
基于定性因果关系的案例推理算法
针对目前基于案例的推理方法预测性能低、可靠性差的局限性,提出了一种基于定性因果关系(CBR-QC)的案例推理算法。在传统定量检索方法的基础上,引入定性推理的相关知识,用定性方法对案例进行定性描述,构建定性方程,通过定性检索得到有需求的案例组。本文采用欧几里得距离相似计算方法和KNN方法,从检索到的定性相似案例中检索到定量相似案例,最后通过对检索到的案例进行适应,得到预测值。
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