Exploring the Boundary Region of Tolerance Rough Sets for Feature Selection

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dc.contributor.author Mac Parthaláin, Neil
dc.contributor.author Shen, Qiang
dc.date.accessioned 2009-01-21T12:29:17Z
dc.date.available 2009-01-21T12:29:17Z
dc.date.issued 2009-01-21
dc.identifier.citation Mac Parthaláin , N & Shen , Q 2009 , ' Exploring the Boundary Region of Tolerance Rough Sets for Feature Selection ' Unknown Journal , pp. 655-667 . en
dc.identifier.other PURE: 107517
dc.identifier.other dspace: 2160/1856
dc.identifier.uri http://hdl.handle.net/2160/1856
dc.description N. Mac Parthaláin, and Q. Shen, Exploring the boundary region of tolerance rough sets for feature selection, Pattern Recognition, vol. 42 , no. 5 , pp. 655-667 , 2009. 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 Unknown Journal 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 Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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