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  • Concept Learning of Text Documents

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    57518_1.pdf (148.0Kb)
    Author(s)
    An, Jiyuan
    Chen, Yi-Ping Phoebe
    Griffith University Author(s)
    An, Jay
    Year published
    2004
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    Abstract
    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 naﶥ 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 ...
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    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 naﶥ 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.
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    Conference Title
    Proceedings: Web Intelligence, 2004. WI 2004
    Publisher URI
    http://ieeexplore.ieee.org/servlet/opac?punumber=9689
    DOI
    https://doi.org/10.1109/WI.2004.10069
    Copyright Statement
    © 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.
    Publication URI
    http://hdl.handle.net/10072/25667
    Collection
    • Conference outputs

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