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Personalized Web Document Classification using MCRDR
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This paper focuses on real world Web document classification problem. Real world Web documents classification has different problems compare to experimental based classification. Web documents have been continually increased and their themes also have been continually changed. Furthermore, domain users' knowledge is not fixed apart from classification environments. They learn from classification experience, broaden their knowledge, and tend to reclassify pre-classified Web documents according to newly obtained knowledge to fit various contexts. To handle these kinds of problems, we use Multiple Classification Ripple-Down Rules (MCRDR) knowledge acquisition method. The MCRDR based document classification enables domain users to elicit their domain knowledge incrementally and revise their knowledge base (KB), and consequently reclassify preclassified documents according to context changes. Our experiment results show MCRDR document classifier performs these tasks successfully in the real world.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||Knowledge Acquisition, Multiple Classification Ripple Down Rules, MCRDR, RDR, Web Document Management System, Document Classification, World Wide Web, Monitoring|
|Publisher:||School of Computing, Unviersity of Tasmania|
|Page Range:||pp. 63-73|
|Date Deposited:||05 Oct 2004|
|Last Modified:||18 Nov 2014 03:10|
|Item Statistics:||View statistics for this item|
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