TRIPPER: Rule learning using taxonomies

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

doi:10.1007/11731139_9

TRIPPER: Rule learning using taxonomies

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

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 language	English (US)
Title of host publication	Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Pages	55-59
Number of pages	5
DOIs	https://doi.org/10.1007/11731139_9
State	Published - 2006
Event	10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore Duration: Apr 9 2006 → Apr 12 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3918 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
Country/Territory	Singapore
City	Singapore
Period	4/9/06 → 4/12/06

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/11731139_9

Cite this

Vasile, F., Silvescu, A., Kang, D. K., & Honavar, V. (2006). TRIPPER: Rule learning using taxonomies. In Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings (pp. 55-59). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3918 LNAI). https://doi.org/10.1007/11731139_9

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abstract = "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.",

author = "Flavian Vasile and Adrian Silvescu and Kang, {Dae Ki} and Vasant Honavar",

year = "2006",

doi = "10.1007/11731139_9",

language = "English (US)",

isbn = "3540332065",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "55--59",

booktitle = "Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings",

note = "10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 ; Conference date: 09-04-2006 Through 12-04-2006",

}

Vasile, F, Silvescu, A, Kang, DK & Honavar, V 2006, TRIPPER: Rule learning using taxonomies. in Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3918 LNAI, pp. 55-59, 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006, Singapore, Singapore, 4/9/06. https://doi.org/10.1007/11731139_9

TRIPPER: Rule learning using taxonomies. / Vasile, Flavian; Silvescu, Adrian; Kang, Dae Ki et al.
Advances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings. 2006. p. 55-59 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3918 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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TRIPPER: Rule learning using taxonomies

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