Show simple item record Jensen, Richard Shen, Qiang 2008-01-21T12:27:28Z 2008-01-21T12:27:28Z 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 PURE UUID: 2e54bfab-3c85-4572-a7ab-d1f0ebac6abb
dc.identifier.other dspace: 2160/441
dc.identifier.other DSpace_20121128.csv: row: 315
dc.identifier.other Scopus: 50249184173
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.rights en
dc.title Tolerance-based and Fuzzy-Rough Feature Selection. en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en

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