Show simple item record MacParthaláin, Neil Seosamh Shen, Qiang 2013-07-23T13:16:20Z 2013-07-23T13:16:20Z 2009-05-01
dc.identifier.citation MacParthaláin , N S & Shen , Q 2009 , ' Exploring the Boundary Region of Tolerance Rough Sets for Feature Selection ' Pattern Recognition , vol. 42 , no. 5 , pp. 655-667 . en
dc.identifier.issn 0031-3203
dc.identifier.other PURE: 98726
dc.identifier.other PURE UUID: 5f21e5d4-f7a2-4884-a509-0ebb7b0120c7
dc.identifier.other dspace: 2160/1856
dc.identifier.other DSpace_20121128.csv: row: 1524
dc.identifier.other RAD: 679
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 2915
dc.identifier.other Scopus: 58249084603
dc.identifier.other 2160/10842
dc.identifier.other ORCID: /0000-0003-1935-2914/work/60054332
dc.identifier.uri en
dc.description MacParthaláin, N. S., Shen, Q. (2009). Exploring the Boundary Region of Tolerance Rough Sets for Feature Selection. Pattern Recognition, 42 (5), 655-667 en
dc.description.abstract Of all of the challenges which face the effective application of computational intelligence technologies for pattern recognition, dataset dimensionality is undoubtedly one of the primary impediments. In order for pattern classifiers to be efficient, a dimensionality reduction stage is usually performed prior to classification. Much use has been made of rough set theory for this purpose as it is completely data-driven and no other information is required; most other methods require some additional knowledge. However, traditional rough set-based methods in the literature are restricted to the requirement that all data must be discrete. It is therefore not possible to consider real-valued or noisy data. This is usually addressed by employing a discretisation method, which can result in information loss. This paper proposes a new approach based on the tolerance rough set model, which has the ability to deal with real-valued data whilst simultaneously retaining dataset semantics. More significantly, this paper describes the underlying mechanism for this new approach to utilise the information contained within the boundary region or region of uncertainty. The use of this information can result in the discovery of more compact feature subsets and improved classification accuracy. These results are supported by an experimental evaluation which compares the proposed approach with a number of existing feature selection techniques. en
dc.format.extent 13 en
dc.language.iso eng
dc.relation.ispartof Pattern Recognition en
dc.rights en
dc.subject Attribute reduction en
dc.subject Feature selection en
dc.subject Classification en
dc.subject Rough sets en
dc.title Exploring the Boundary Region of Tolerance Rough Sets for Feature Selection en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
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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