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.
|Translated title of the contribution||An Analysis of Stopping and Filtering Criteria for Rule Learning|
|Title of host publication||Proceedings of the 15th European Conference on Machine Learning|
|Publication status||Published - 2004|
Bibliographical noteISBN: 3540231056
Name and Venue of Conference: Proceedings of the 15th European Conference on Machine Learning
Other identifier: 2000550