Earticle

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

ABSTRACT
 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
 

저자

• Yoon-Joo Park [ Assistant Professor, Seoul National University of Science and Technology, Korea ] Corresponding Author

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