Maintaining Case-Based Reasoning Systems Using Fuzzy
Decision Trees1

Simon Chi Keung Shiu, Cai Hung Sun, Xi Zhao Wang and Daniel So Yeung

Department of Computing. Hong Kong Polytechnic University
Hung Horn, Kowloon, Hong Kong
{csckshiu,cschsun,csxzwang,csdaniel}@comp.polyu.edu.hk


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 e -coverage and e -reachability is used to
select representative cases. The effectiveness of the method is
demonstrated experimentally using two sets of testing data.
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