Using Decision Trees to Improve Case-Based Learning

This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-exible prediction that lies between the classiication task performed by decision tree algorithms and the exible prediction task performed by conceptual clustering systems. In semi-exible prediction, learning should improve prediction of a spe-ciic set of features known a priori rather than a single known feature (as in classii-cation) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge.