Rough feature selection for intelligent classifiers

H...............H

Show simple item record

dc.contributor.author Shen, Qiang
dc.contributor.editor Peters, James F.
dc.contributor.editor Skowron, Andrzej
dc.contributor.editor Marek, Victor W.
dc.contributor.editor Orłowska, Ewa
dc.contributor.editor Słowiński, Roman
dc.contributor.editor Ziarko, Wojciech
dc.date.accessioned 2008-01-15T14:55:02Z
dc.date.available 2008-01-15T14:55:02Z
dc.date.issued 2007
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 Berlin Heidelberg , pp. 244-255 . en
dc.identifier.isbn 978-3-540-71662-4
dc.identifier.isbn 978-3-540-71663-1
dc.identifier.issn 1861-2059
dc.identifier.other PURE: 1674424
dc.identifier.other dspace: 2160/420
dc.identifier.uri http://hdl.handle.net/2160/420
dc.identifier.uri http://www.springerlink.com/content/06p30r86x5705114/fulltext.pdf en
dc.description Q. Shen. Rough feature selection for intelligent classifiers. LNCS Transactions on Rough Sets, 7:244-255, 2007. en
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
dc.format.extent 12 en
dc.language.iso eng
dc.publisher Springer Berlin Heidelberg
dc.relation.ispartof Transactions on Rough Sets VII en
dc.relation.ispartofseries Lecture Notes in Computer Science en
dc.title Rough feature selection for intelligent classifiers en
dc.type Text en
dc.type.publicationtype Book chapter en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

My Account