Multiple Sets of Rules for Text Categorization

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This paper concerns how multiple sets of rules can be generated using a rough sets-based inductive learning method and how they can be combined for text categorization by 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 model outcomes inferred from rules as pieces of evidence. The various experiments have been carried out on 10 out of the 20-newsgroups – a benchmark data collection – individually and in combination. Our experimental results support the claim that “k experts may be better than any one if their individual judgements are appropriately combined”.