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Multiple Sets of Rules for Text Categorization

Bi, Y, Anderson, TJ and McClean, SI (2004) Multiple Sets of Rules for Text Categorization. In: Advances in Information Systems 2004, Izmir, Turkey. Springer. 10 pp. [Conference contribution]

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URL: http://www.springerlink.com/index/X95B2KA5Y1PC925M

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.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
ID Code:8297
Deposited By: Dr Yaxin Bi
Deposited On:17 Apr 2013 13:20
Last Modified:17 Apr 2013 13:20

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