Depth first rule heneration for text categorization

An, Jiyuan and Chen, Yi-Ping Phoebe 2006, Depth first rule heneration for text categorization, Frontiers in artificial intelligence and applications: advances in intelligent IT: active media technology, vol. 138, pp. 302-306.

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Title Depth first rule heneration for text categorization
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Journal name Frontiers in artificial intelligence and applications: advances in intelligent IT: active media technology
Volume number 138
Start page 302
End page 306
Publisher IOS Press
Place of publication Amsterdam, Netherlands
Publication date 2006
ISSN 0922-6389
1879-8314
Summary Classification methods are usually used to categorize text documents, such as, Rocchio method, Naïve bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct classifiers. The generated classifiers can predict which category is located for a new coming text document. The keywords in the document are often used to form rules to categorize text documents, for example “kw = computer” can be a rule for the IT documents category. However, the number of keywords is very large. To select keywords from the large number of keywords is a challenging work. Recently, a rule generation method based on enumeration of all possible keywords combinations has been proposed [2]. In this method, there remains a crucial problem: how to prune irrelevant combinations at the early stages of the rule generation procedure. In this paper, we propose a method than can effectively prune irrelative keywords at an early stage.
Language eng
Field of Research 080610 Information Systems Organisation
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, The authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003694

Document type: Journal Article
Collection: School of Engineering and Information Technology
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