Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9307
Title: Transferring case knowledge to adaptation knowledge: An approach for case-base maintenance
Authors: Shiu, SCK 
Yeung, DS
Sun, CH
Wang, XZ
Keywords: Case-base maintenance
Fuzzy decision trees
Knowledge containers
Issue Date: 2001
Publisher: Wiley-Blackwell
Source: Computational intelligence, 2001, v. 17, no. 2, p. 295-314 How to cite?
Journal: Computational intelligence 
Abstract: In this article we propose a case-base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case-based reasoning (CBR) system. A machine-learning technique, fuzzy decision-tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature-weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.
URI: http://hdl.handle.net/10397/9307
ISSN: 0824-7935
EISSN: 1467-8640
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