Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/4243
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dc.contributor.authorMafarja, Majdi
dc.contributor.authorEleyan, Derar
dc.date.accessioned2017-02-14T07:45:21Z
dc.date.available2017-02-14T07:45:21Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/20.500.11889/4243
dc.description.abstractFeature selection is an important concept in rough set theory; it aims to determine a minimal subset of features that are jointly sufficient for preserving a particular property of the original data. This paper proposes an attribute reduction method that is based on Ant Colony Optimization algorithm and rough set theory as an evaluation measurement. The proposed method was tested on standard benchmark datasets. The results show that this algorithm performs well and competes other attribute reduction approaches in terms of the number of the selected features and the running time.en_US
dc.language.isoen_USen_US
dc.subjectRough setsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectSet theoryen_US
dc.subjectMathematical optimizationen_US
dc.subjectAnts - Behavior - Mathematical modelsen_US
dc.titleAnt colony optimization based feature selection in rough set theoryen_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectnoneen_US
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item.languageiso639-1other-
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