Intelligent Data Engineering and Automated Learning – IDEAL 2004

Volume 3177 of the series Lecture Notes in Computer Science pp 457-463

Combining Rules for Text Categorization Using Dempster’s Rule of Combination

  • Yaxin BiAffiliated withSchool of Computer Science, Queen’s University of BelfastSchool of Biomedical Science, University of Ulster
  • , Terry AndersonAffiliated withFaculty of Engineering, University of Ulster
  • , Sally McCleanAffiliated withFaculty of Engineering, University of Ulster

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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.