TRIPPER: Rule learning using taxonomies

Flavian Vasile, Adrian Silvescu, Dae Ki Kang, Vasant Honavar

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

5 Scopus citations


In many application domains, there is a need for learning algorithms that generate accurate as well as comprehensible classifiers. In this paper, we present TRIPPER - a rule induction algorithm that extends RIPPER, a widely used rule-learning algorithm. TRIPPER exploits knowledge in the form of taxonomies over the values of features used to describe data. We compare the performance of TRIPPER with that of RIPPER on benchmark datasets from the Reuters 21578 corpus using WordNet (a human-generated taxonomy) to guide rule induction by TRIPPER. Our experiments show that the rules generated by TRIPPER are generally more comprehensible and compact and in the large majority of cases at least as accurate as those generated by RIPPER.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Number of pages5
StatePublished - 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: Apr 9 2006Apr 12 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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