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