基于粗糙集理论的CBR检索方法研究

2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS) Pub Date : 2015-11-30 DOI:10.1109/ICSESS.2015.7339220

Hui Li, Ye Song, Xiaoping Li, Qiongxin Liu, Yuanfang Zhu

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引用次数: 3

摘要

案例检索是案例推理的关键技术，它直接影响到案例推理的效率和质量。对于类似情况下的度量问题，利用粗糙集理论确定属性的重要程度，分配各属性的合理权值。采用基于汉明距离和欧几里得距离结合的改进最近邻法求解案例相似度，提高了案例匹配的精度和效率。

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Research of CBR retrieval method based on rough set theory

Case retrieval is the key technology of case-based reasoning (CBR), directly affect the efficiency and quality of CBR. For the measurement issues of similar cases, using rough set theory to determine the importance of attributes and to distribute the rational weights of each property. Taking the improved nearest neighbor method which is based on the combination of Hamming distance and Euclidean distance to solve case similarity, improve the accuracy and efficiency of case matching.

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2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS)

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