Difference-similitude matrix in text classification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II(2005)
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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Key words
text classification,classification approach,DSM reduction algorithm,high dimensionality,information retrieval,rough set reduction,Difference-Similitude Matrix,different category,document space,item-document matrix,Difference-similitude matrix
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