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.

• Type: Conference
• Year: 2000
• Where: EWCBR
• Authors: Simon C. K. Shiu, Cai Hung Sun, Xizhao Wang, Daniel S. Yeung
• Keywords: Adaptation Rules, Automated Reasoning, Case Library, EWCBR 2000, Fuzzy Decision Trees