A Case-Based Reasoning Method with Rank Aggregation

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Sun, Jinhua^1 ; Du, Jiao^1 ; Hu, Jian^1

School of Management, Chongqing University of Technology, Chongqing, China^1

关键词: Basic principles;    Case retrieval;    Casebased reasonings (CBR);    Euclidean distance;    Mahalanobis distances;    Optimal problems;    Rank aggregation;    Ranking methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/6/062023/pdf
DOI  :  10.1088/1757-899X/322/6/062023

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In order to improve the accuracy of case-based reasoning (CBR), this paper addresses a new CBR framework with the basic principle of rank aggregation. First, the ranking methods are put forward in each attribute subspace of case. The ordering relation between cases on each attribute is got between cases. Then, a sorting matrix is got. Second, the similar case retrieval process from ranking matrix is transformed into a rank aggregation optimal problem, which uses the Kemeny optimal. On the basis, a rank aggregation case-based reasoning algorithm, named RA-CBR, is designed. The experiment result on UCI data sets shows that case retrieval accuracy of RA-CBR algorithm is higher than euclidean distance CBR and mahalanobis distance CBR testing.So we can get the conclusion that RA-CBR method can increase the performance and efficiency of CBR.