An Analysis of Stopping and Filtering Criteria for Rule Learning

Abstract. In this paper, we investigate the properties of commonly used prepruning 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.

• Type: Conference
• Year: 2004
• Where: ECML
• Authors: Johannes Fürnkranz, Peter A. Flach
• Keywords: Better Understanding, ECML 2004, Heuristics, Rule Learning