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Concept learning of text documents

An, Jiyuan and Chen, Yi-Ping Phoebe 2004, Concept learning of text documents, in IEEE/WIC International Conference on Web Intelligence (WI 2004) : Beijing, China, September 20-24, 2004 : proceedings, IEEE Xplore, Piscataway, N.J., pp. 698-701.

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Title Concept learning of text documents
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Conference name IEEE/WIC/ACM International Conference on Intelligent Agent Technology (2004 : Beijing, China)
Conference location Beijing, China
Conference dates September 20-24 2004
Title of proceedings IEEE/WIC International Conference on Web Intelligence (WI 2004) : Beijing, China, September 20-24, 2004 : proceedings
Editor(s) Zhong, Ning
Tirri, Henry
Yao, Yiyu
Zhou, Lizhu
Liu, Jiming
Cercone, Nick
Publication date 2004
Start page 698
End page 701
Publisher IEEE Xplore
Place of publication Piscataway, N.J.
Summary Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A naïve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods.
ISBN 0769521002
9780769521008
Language eng
Field of Research 080699 Information Systems not elsewhere classified
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005267

Document type: Conference Paper
Collections: School of Information Technology
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