Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/4244
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dc.contributor.authorMafarja, Majdi
dc.contributor.authorAbdullah, Salwani
dc.contributor.authorJaddi, Najmeh S.
dc.date.accessioned2017-02-14T08:01:25Z
dc.date.available2017-02-14T08:01:25Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/20.500.11889/4244
dc.description.abstractOne of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.en_US
dc.language.isoen_USen_US
dc.subjectRough setsen_US
dc.subjectSet theoryen_US
dc.subjectArtificial intelligenceen_US
dc.subjectFuzzy logicen_US
dc.subjectAlgorithmsen_US
dc.titleFuzzy population-based meta-heuristic approaches for attribute reduction 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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