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2000
Springer

Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees

9 years 3 months ago
Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees
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 -coverage and -reachability is used to select representative cases. The effectiveness of the method is demonstrated experimentally using two sets of testing data.
Simon C. K. Shiu, Cai Hung Sun, Xizhao Wang, Danie
Added 24 Aug 2010
Updated 24 Aug 2010
Type Conference
Year 2000
Where EWCBR
Authors Simon C. K. Shiu, Cai Hung Sun, Xizhao Wang, Daniel S. Yeung
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