Abstract
Text classification can greatly improve the performance of information retrieval and information filtering, but high dimensionality of documents baffles the applications of most classification approaches. This paper proposed a Difference-Similitude Matrix (DSM) based method to solve the problem. The method represents a pre-classified collection as an item-document matrix, in which documents in same categories are described with similarities while documents in different categories with differences. Using the DSM reduction algorithm, simpler and more efficient than rough set reduction, we reduced the dimensionality of document space and generated rules for text classification.
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Huang, X., Wu, M., Xia, D., Yan, P. (2005). Difference-Similitude Matrix in Text Classification. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_3
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DOI: https://doi.org/10.1007/11540007_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
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