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http://dx.doi.org/10.3745/KTSDE.2013.2.8.535

Optimizing Similarity Threshold and Coverage of CBR  

Ahn, Hyunchul (국민대학교 경영정보학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.8, 2013 , pp. 535-542 More about this Journal
Abstract
Since case-based reasoning(CBR) has many advantages, it has been used for supporting decision making in various areas including medical checkup, production planning, customer classification, and so on. However, there are several factors to be set by heuristics when designing effective CBR systems. Among these factors, this study addresses the issue of selecting appropriate neighbors in case retrieval step. As the criterion for selecting appropriate neighbors, conventional studies have used the preset number of neighbors to combine(i.e. k of k-nearest neighbor), or the relative portion of the maximum similarity. However, this study proposes to use the absolute similarity threshold varying from 0 to 1, as the criterion for selecting appropriate neighbors to combine. In this case, too small similarity threshold value may make the model rarely produce the solution. To avoid this, we propose to adopt the coverage, which implies the ratio of the cases in which solutions are produced over the total number of the training cases, and to set it as the constraint when optimizing the similarity threshold. To validate the usefulness of the proposed model, we applied it to a real-world target marketing case of an online shopping mall in Korea. As a result, we found that the proposed model might significantly improve the performance of CBR.
Keywords
Case-based Reasoning; Genetic Algorithm; Similarity Threshold; Coverage;
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Times Cited By KSCI : 3  (Citation Analysis)
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