Using Decision Trees to Improve Case-Based Learning (1993)
| Venue: | In Proceedings of the Tenth International Conference on Machine Learning |
| Citations: | 85 - 8 self |
BibTeX
@INPROCEEDINGS{Cardie93usingdecision,
author = {Claire Cardie},
title = {Using Decision Trees to Improve Case-Based Learning},
booktitle = {In Proceedings of the Tenth International Conference on Machine Learning},
year = {1993},
pages = {25--32},
publisher = {Morgan Kaufmann}
}
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Abstract
This paper shows that decision trees can be used to improve the performance of casebased learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) 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 ...
