2004 | OriginalPaper | Buchkapitel
An Analysis of Stopping and Filtering Criteria for Rule Learning
verfasst von : Johannes Fürnkranz, Peter Flach
Erschienen in: Machine Learning: ECML 2004
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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.