An association-based case reduction technique for case-based reasoning

作者：Liu Cheng Hsiang^*; Chen Long Sheng; Hsu Chun Chin

来源：Information Sciences, 2008, 178(17): 3347-3355.

DOI：10.1016/j.ins.2008.05.006

• case-based reasoning
• case reduction
• case base maintenance
• classification
• association rules

Case-based reasoning (CBR) is a type of problem solving technique which uses previous cases to solve new, unseen and different problems. Although a larger number of cases in the memory can improve the coverage of the problem space, the retrieval efficiency will be downgraded if the size of the case-base grows to an unacceptable level. In CBR systems, the tradeoff between the number of cases stored in the case-base and the retrieval efficiency is a critical issue. This paper addresses the problem of case-base maintenance by developing a new technique, the association-based case reduction technique (ACRT), to reduce the size of the case-base in order to enhance the efficiency while maintaining or even improving the accuracy of the CBR. The experiments on 12 UCI datasets and an actual case from Taiwan's hospital have shown superior generalization accuracy for CBR with ACRT (CBR-ACRT) as well as a greater solving efficiency.

• 出版日期2008-9-1