Fuzzy-rough instance selection


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dc.contributor.author Jensen, Richard
dc.contributor.author Cornelis, Chris
dc.date.accessioned 2010-07-19T15:46:44Z
dc.date.available 2010-07-19T15:46:44Z
dc.date.issued 2010
dc.identifier.citation Jensen , R & Cornelis , C 2010 , ' Fuzzy-rough instance selection ' pp. 1776-1782 . en
dc.identifier.other PURE: 150125
dc.identifier.other dspace: 2160/4824
dc.identifier.uri http://hdl.handle.net/2160/4824
dc.description R. Jensen and C. Cornelis. Fuzzy-rough instance selection. Proceedings of the 19th International Conference on Fuzzy Systems (FUZZ-IEEE’10), pp. 1776-1782, 2010. en
dc.description.abstract Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Since its introduction, this theory has been successfully utilised to devise mathematically sound and often, computationally efficient techniques for addressing problems such as hidden pattern discovery from data, feature selection and decision rule generation. Fuzzy-rough set theory improves upon this by enabling uncertainty and vagueness to be modeled more effectively. Recently, the value of fuzzy-rough sets for feature selection and rule induction has been established. However, the potential of this theory for instance selection has not been investigated at all. This paper proposes three novel methods for instance selection based on fuzzy-rough sets. The initial experimentation demonstrates that the methods can significantly reduce the number of instances whilst maintaining high classification accuracies. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Fuzzy-rough instance 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 Peer reviewed en

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