William W. Cohen and Yoram Singer, AT&T Labs - Research
We describe SLIPPER, a new rule learner that generates rulesets by repeatedly boosting a simple, greedy, rule-builder. The ensemble of rules created by SLIPPER is compact and comprehensible, like the rulesets built by other rule learners. This is made possible by imposing appropriate constraints on the rule-builder, and by use of a recently-proposed generalization of Adaboost called confidence-rated boosting. In spite of its relative simplicity, SLIPPER is highly scalable, and an effective learner. Experimentally, SLIPPER scales no worse than O(nlogn), where n is the number of examples, and on a set of 32 benchmark problems, SLIPPER achieves lower error rates than RIPPER 20 times, and lower error rates than C4.5rules 22 times.