Abstract
This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction - a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees. Firstly, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases will be carried out to identify different concepts in the case-base using the acquired feature knowledge. Thirdly, adaptation rules will be mined for each concept using fuzzy decision trees. Finally, a selection strategy based on the concepts of ε-coverage and ε-reachability is used to select representative cases. The effectiveness of the method is demonstrated experimentally using two sets of testing data.
This project is supported by a HK PolyU grant PA25
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Shiu, S.C.K., Sun, C.H., Wang, X.Z., Yeung, D.S. (2000). Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_25
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DOI: https://doi.org/10.1007/3-540-44527-7_25
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