Bi, Y, Anderson, TJ and McClean, SI (2004) Combining Rules for Text Categorization Using Dempster's Rule of Combination. In: Intelligent Data Engineering and Automated Learning - IDEAL 2004, Exeter, UK. Springer. Vol 3177/2 7 pp. [Conference contribution]
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In this paper, we present an investigation into the combination of rules for text categorization using Dempsters 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 Dempsters 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 Mathematics
Faculty of Computing & Engineering > School of Computing and Information Engineering
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Information and Communication Engineering
|Deposited By:||Professor Sally McClean|
|Deposited On:||02 Jul 2010 10:23|
|Last Modified:||09 May 2016 10:53|
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