Abstract
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”.
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© 2004 Springer-Verlag Berlin Heidelberg
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Bi, Y., Anderson, T., McClean, S. (2004). Multiple Sets of Rules for Text Categorization. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2004. Lecture Notes in Computer Science, vol 3261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30198-1_27
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DOI: https://doi.org/10.1007/978-3-540-30198-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23478-4
Online ISBN: 978-3-540-30198-1
eBook Packages: Computer ScienceComputer Science (R0)