Induced decision trees for case-based reasoning

The paper examines application of a machine learning technique, decision tree induction, to the development of CBR systems. The decision tree generated by the induction algorithm is generally used to create a set of rules for a traditional rule based system; however, it may be used to determine the weight (i.e., importance) of features for classification. This approach combines the flexibility of CBR systems with the machine learning ability to automatically derive decision criteria. We experimentally assess the tolerance of rule induction to noise and missing values and find it surprisingly resilient to poor quality training data. This bodes well for use of rule induction to allow the process of CBR system development to be more independent of expert judgement