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dc.contributor.author Qiang en_US
dc.contributor.editor James F. en_US
dc.contributor.editor Andrzej en_US
dc.contributor.editor Victor W. en_US
dc.contributor.editor Ewa en_US
dc.contributor.editor Roman en_US
dc.contributor.editor Wojciech en_US
dc.date.accessioned 2008-01-15T14:55:02Z
dc.date.available 2008-01-15T14:55:02Z
dc.date.issued 2007 en_US
dc.identifier 978-3-540-71662-4 en_US
dc.identifier 978-3-540-71663-1 en_US
dc.identifier.citation Shen , Q 2007 , ' Rough feature selection for intelligent classifiers ' . in J F Peters , A Skowron , V W Marek , E Orłowska , R Słowiński & W Ziarko (eds) , Transactions on Rough Sets VII : Commemorating the Life and Work of Zdzisław Pawlak, Part I . vol. 4400 , Lecture Notes in Computer Science , vol. 4400 , Springer Science+Business Media , pp. 244-255 . en_US
dc.identifier.other PURE: 1674424 en_US
dc.identifier.other dspace: 2160/420 en_US
dc.identifier.uri http://hdl.handle.net/2160/420
dc.description.abstract The last two decades have seen many powerful classification systems being built for large-scale real-world applications. However, for all their accuracy, one of the persistent obstacles facing these systems is that of data dimensionality. To enable such systems to be effective, a redundancy-removing step is usually required to pre-process the given data. Rough set theory offers a useful, and formal, methodology that can be employed to reduce the dimensionality of datasets. It helps select the most information rich features in a dataset, without transforming the data, all the while attempting to minimise information loss during the selection process. Based on this observation, this paper discusses an approach for semantics-preserving dimensionality reduction, or feature selection, that simplifies domains to aid in developing fuzzy or neural classifiers. Computationally, the approach is highly efficient, relying on simple set operations only. The success of this work is illustrated by applying it to addressing two real-world problems: industrial plant monitoring and medical image analysis. en_US
dc.format.extent 12 en_US
dc.publisher Springer Science+Business Media en_US
dc.relation.ispartof Transactions on Rough Sets VII en_US
dc.relation.ispartofseries Lecture Notes in Computer Science en_US
dc.title Rough feature selection for intelligent classifiers en_US
dc.contributor.pbl Department of Computer Science en_US
dc.contributor.pbl Advanced Reasoning Group en_US


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