Multiple Sets of Rules for Text Categorization

Y Bi, TJ Anderson, SI McClean

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

Abstract

. In this paper, we present an investigation into the combination of rules for text categorization using Dempster’s rule of combination. We first propose a boosting-like technique for generating multiple sets of rules based on rough set theory, and then describe how to use Dempster’s rule of combination to combine the classification decisions produced by multiple sets of rules. We apply these methods to 10 out of the 20-newsgroups – a benchmark data collection, individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data can achieve 80.47% classification accuracy, which is 3.24% better than that of the best single set of rules.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages263-272
Number of pages10
Publication statusPublished - Oct 2004
EventAdvances in Information Systems 2004 - Izmir, Turkey
Duration: 1 Oct 2004 → …

Conference

ConferenceAdvances in Information Systems 2004
Period1/10/04 → …

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Rough set theory

Cite this

Bi, Y., Anderson, TJ., & McClean, SI. (2004). Multiple Sets of Rules for Text Categorization. In Unknown Host Publication (pp. 263-272)
Bi, Y ; Anderson, TJ ; McClean, SI. / Multiple Sets of Rules for Text Categorization. Unknown Host Publication. 2004. pp. 263-272
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author = "Y Bi and TJ Anderson and SI McClean",
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Bi, Y, Anderson, TJ & McClean, SI 2004, Multiple Sets of Rules for Text Categorization. in Unknown Host Publication. pp. 263-272, Advances in Information Systems 2004, 1/10/04.

Multiple Sets of Rules for Text Categorization. / Bi, Y; Anderson, TJ; McClean, SI.

Unknown Host Publication. 2004. p. 263-272.

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

TY - GEN

T1 - Multiple Sets of Rules for Text Categorization

AU - Bi, Y

AU - Anderson, TJ

AU - McClean, SI

PY - 2004/10

Y1 - 2004/10

N2 - . In this paper, we present an investigation into the combination of rules for text categorization using Dempster’s rule of combination. We first propose a boosting-like technique for generating multiple sets of rules based on rough set theory, and then describe how to use Dempster’s rule of combination to combine the classification decisions produced by multiple sets of rules. We apply these methods to 10 out of the 20-newsgroups – a benchmark data collection, individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data can achieve 80.47% classification accuracy, which is 3.24% better than that of the best single set of rules.

AB - . In this paper, we present an investigation into the combination of rules for text categorization using Dempster’s rule of combination. We first propose a boosting-like technique for generating multiple sets of rules based on rough set theory, and then describe how to use Dempster’s rule of combination to combine the classification decisions produced by multiple sets of rules. We apply these methods to 10 out of the 20-newsgroups – a benchmark data collection, individually and in combination. Our experimental results show that the performance of the best combination of the multiple sets of rules on the 10 groups of the benchmark data can achieve 80.47% classification accuracy, which is 3.24% better than that of the best single set of rules.

M3 - Conference contribution

SP - 263

EP - 272

BT - Unknown Host Publication

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Bi Y, Anderson TJ, McClean SI. Multiple Sets of Rules for Text Categorization. In Unknown Host Publication. 2004. p. 263-272