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Classification Algorithm of Case Retrieval Based on Granularity Calculation of Quotient Space
International Journal of Pattern Recognition and Artificial Intelligence ( IF 1.5 ) Pub Date : 2020-08-08 , DOI: 10.1142/s0218001421500038
Jiaxing Lu 1 , Qing Jiang 2 , He Huang 2 , Zhengyong Zhang 2 , Rujing Wang 2
Affiliation  

Case retrieval is one of the key steps of case-based reasoning. The quality of case retrieval determines the effectiveness of the system. The common similarity calculation methods based on attributes include distance and inner product. Different similarity calculations have different influences on the effect of case retrieval. How to combine different similarity calculation results to get a more widely used and better retrieval algorithm is a hot issue in the current case-based reasoning research. In this paper, the granularity of quotient space is introduced into the similarity calculation based on attribute, and a case retrieval algorithm based on granularity synthesis theory is proposed. This method first uses similarity calculation of different attributes to get different results of case retrieval, and considers that these classification results constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the classification results of case retrieval. The experimental results verify the validity and correctness of this method and the application potential of granularity calculation of quotient space in case-based reasoning.

中文翻译:

基于商空间粒度计算的案例检索分类算法

案例检索是案例推理的关键步骤之一。案例检索的质量决定了系统的有效性。常用的基于属性的相似度计算方法包括距离和内积。不同的相似度计算对案例检索的效果有不同的影响。如何结合不同的相似度计算结果得到更广泛的、更好的检索算法是当前基于案例的推理研究的热点问题。本文将商空间的粒度引入到基于属性的相似度计算中,提出了一种基于粒度合成理论的案例检索算法。该方法首先利用不同属性的相似度计算得到不同的案例检索结果,并认为这些分类结果构成了不同的商空间,然后根据粒度综合理论对这些商空间进行组织,得到案例检索的分类结果。实验结果验证了该方法的有效性和正确性以及商空间粒度计算在案例推理中的应用潜力。
更新日期:2020-08-08
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