TRIPPER: Rule learning using taxonomies

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

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

2 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 background knowledge in the form of taxonomies over values of features used to describe data. We compare the performance of TRIPPER with that of RIPPER on a text classification problem (using the Reuters 21578 dataset). Experiments were performed using WordNet (a human-generated taxonomy), as well as a taxonomy generated by WTL (Word Taxonomy Learning) algorithm. Our experiments show that the rules generated by TRIPPER are generally more accurate and more concise (and hence more comprehensible) than those generated by RIPPER.

Original languageEnglish (US)
Title of host publicationAAAI Workshop - Technical Report
Number of pages8
StatePublished - 2005
EventAAAI-05 Workshop - Pittsburgh, PA, United States
Duration: Jul 9 2005Jul 9 2005

Publication series

NameAAAI Workshop - Technical Report


OtherAAAI-05 Workshop
Country/TerritoryUnited States
CityPittsburgh, PA

All Science Journal Classification (ASJC) codes

  • General Engineering


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