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An Analysis of Stopping and Filtering Criteria for Rule Learning

Johannes Fuernkranz, Peter Flach, An Analysis of Stopping and Filtering Criteria for Rule Learning. Proceedings of the 15th European Conference on Machine Learning. ISBN 3-540-23105-6, pp. 123–133. September 2004. PDF, 390 Kbytes.

Abstract

In this paper, we investigate the properties of commonly used pre- pruning heuristics for rule learning by visualizing them in PN-space. PN-space is a variant of ROC-space, which is particularly suited for visualizing the behavior of rule learning and its heuristics. On the one hand, we think that our results lead to a better understanding of the effects of stopping and filtering criteria, and hence to a better understanding of rule learning algorithms in general. On the other hand, we uncover a few shortcomings of commonly used heuristics, thereby hopefully motivating additional work in this area.

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