Simple, fast, and effective rule learner

William W. Cohen, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

227 Scopus citations

Abstract

We describe SLIPPER, a new rule learner that generates rulesets by repeatedly boosting a simple, greedy, rule-builder. Like the rulesets built by other rule learners, the ensemble of rules created by SLIPPER is compact and comprehensible. 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(n log n), 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.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAAAI
Pages335-342
Number of pages8
ISBN (Print)0262511061
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99) - Orlando, FL, USA
Duration: Jul 18 1999Jul 22 1999

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Other

OtherProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99)
CityOrlando, FL, USA
Period7/18/997/22/99

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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