Show simple item record

dc.contributor.author Jensen, Richard
dc.contributor.author Shen, Qiang
dc.date.accessioned 2008-01-21T12:27:28Z
dc.date.available 2008-01-21T12:27:28Z
dc.date.issued 2007
dc.identifier.citation Jensen , R & Shen , Q 2007 , ' Tolerance-based and Fuzzy-Rough Feature Selection. ' pp. 877-882 . en
dc.identifier.other PURE: 74171
dc.identifier.other dspace: 2160/441
dc.identifier.uri http://hdl.handle.net/2160/441
dc.description R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007. en
dc.description.abstract One of the main obstacles facing the application of computational intelligence technologies in pattern recognition (and indeed in many other tasks) is that of dataset dimensionality. To enable pattern classifiers to be effective, a dimensionality minimization step is usually carried out beforehand. Rough set theory has been successfully applied for this as it requires only the supplied data and no other information; most other methods require supplementary knowledge. However, the main limitation of traditional rough set-based selection in the literature is the restrictive requirement that all data is discrete; it is not possible to consider real-valued or noisy data. This has been tackled previously via the use of discretization methods, but may result in information loss. This paper investigates two approaches based on rough set extensions, namely fuzzy-rough and tolerance rough sets, that address these problems and retain dataset semantics. The methods are compared experimentally and utilized for the task of forensic glass fragment identification. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Tolerance-based and Fuzzy-Rough Feature Selection. en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


Files in this item

Aside from theses and in the absence of a specific licence document on an item page, all works in Cadair are accessible under the CC BY-NC-ND Licence. AU theses and dissertations held on Cadair are made available for the purposes of private study and non-commercial research and brief extracts may be reproduced under fair dealing for the purpose of criticism or review. If you have any queries in relation to the re-use of material on Cadair, contact is@aber.ac.uk.

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

Statistics