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dc.contributor.author Jensen, Richard
dc.contributor.author Shen, Qiang
dc.date.accessioned 2008-01-15T14:54:05Z
dc.date.available 2008-01-15T14:54:05Z
dc.date.issued 2004
dc.identifier.citation Jensen , R & Shen , Q 2004 , ' Fuzzy-Rough Attribute Reduction with Application to Web Categorization. ' Fuzzy Sets and Systems , vol 141 , no. 3 , pp. 469-485 . , 10.1016/S0165-0114(03)00021-6 en
dc.identifier.issn 0165-0114
dc.identifier.other PURE: 74056
dc.identifier.other dspace: 2160/419
dc.identifier.uri http://hdl.handle.net/2160/419
dc.description R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004. en
dc.description.abstract Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents a novel approach, based on an integrated use of fuzzy and rough set theories, to greatly reduce this data redundancy. Formal concepts of fuzzy-rough attribute reduction are introduced and illustrated with a simple example. The work is applied to the problem of web categorization, considerably reducing dimensionality with minimal loss of information. Experimental results show that fuzzy-rough reduction is more powerful than the conventional rough set-based approach. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. en
dc.format.extent 17 en
dc.language.iso eng
dc.relation.ispartof Fuzzy Sets and Systems en
dc.subject Attribute reduction en
dc.subject Web categorization en
dc.subject Data redundancy en
dc.title Fuzzy-Rough Attribute Reduction with Application to Web Categorization. en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/S0165-0114(03)00021-6
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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