Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring.

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dc.contributor.author Shen, Qiang
dc.contributor.author Jensen, Richard
dc.date.accessioned 2008-01-28T11:12:49Z
dc.date.available 2008-01-28T11:12:49Z
dc.date.issued 2004-07
dc.identifier.citation Shen , Q & Jensen , R 2004 , ' Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring. ' Pattern Recognition , vol 37 , no. 7 , pp. 1351-1363 . , 10.1016/j.patcog.2003.10.016 en
dc.identifier.issn 0031-3203
dc.identifier.other PURE: 74079
dc.identifier.other dspace: 2160/487
dc.identifier.uri http://hdl.handle.net/2160/487
dc.description Q. Shen and R. Jensen, 'Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring,' Pattern Recognition, vol. 37, no. 7, pp. 1351-1363, 2004. en
dc.description.abstract One of the main obstacles facing current intelligent pattern recognition applications is that of dataset dimensionality. To enable these systems to be effective, a redundancy-removing step is usually carried out beforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantization of the underlying numerical features. This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss. The current work retains dataset semantics, allowing for the creation of clear, readable fuzzy models. Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-based, PCA-based and random-based methods. en
dc.format.extent 13 en
dc.language.iso eng
dc.relation.ispartof Pattern Recognition en
dc.subject Feature selection en
dc.subject Feature dependency en
dc.subject Fuzzy-rough sets en
dc.subject Reduct search en
dc.subject Rule induction en
dc.subject Systems monitoring en
dc.title Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring. en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.patcog.2003.10.016
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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