원문정보
초록
영어
Conventional case-based reasoning (CBR) does not perform efficiently for high-volume datasets because of case retrieval time. To overcome this problem, previous research suggested clustering a case base into several small groups and retrieving neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performance than the conventional CBR. This paper proposes a new case-based reasoning method called the clustering–merging CBR (CM-CBR). The CM-CBR method dynamically indexes a search pool to retrieve neighbors considering the distance between a target case and the centroid of a corresponding cluster. This method is applied to three real-life medical datasets. Results show that the proposed CM-CBR method produces similar or better predictive performance than the conventional CBR and clustering-CBR methods in numerous cases with significantly less computational cost.
목차
Ⅰ. Introduction
Ⅱ. Related Research
Ⅲ. The Issue of the Clustering Case-Based Reasoning (C-CBR) Methods
Ⅳ. The Clustering-Merging Case-Based Reasoning Method (CM-CBR)
4.1. Determining the Center and Boundary Areas
4.2. Determining the Number of Clusters
4.3. The Overall Procedure of the CM-CBR
Ⅴ. Experiments
5.1. Experimental Settings
5.2. Experimental Results
Ⅵ. Concluding Remarks and Future Work