Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5564
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dc.contributor.authorMafarja, Majdi
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2018-06-12T07:51:05Z
dc.date.available2018-06-12T07:51:05Z
dc.date.issued2018-06-09
dc.identifier.citationMafarja, M.M. & Mirjalili, S. Soft Comput (2018). https://doi.org/10.1007/s00500-018-3282-yen_US
dc.identifier.otherhttps://doi.org/10.1007/s00500-018-3282-y
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5564
dc.description.abstractFeature selection (FS) can be defined as the problem of finding the minimal number of features from an original set with the minimum information loss. Since FS problems are known as NP-hard problems, it is necessary to investigate a fast and an effective search algorithm to tackle this problem. In this paper, two incremental hill-climbing techniques (QuickReduct and CEBARKCC) are hybridized with the Binary Ant Lion Optimizer in a model called (HBALO). In the proposed approach, a pool of solutions (ants) is generated randomly and then enhanced by embedding the most informative features in the dataset that are selected by the two filter feature selection models. The resultant population is then used by BALO algorithm to find the best solution. The proposed binary approaches are tested on a set of 18 well-known datasets from UCI repository and compared with the most recent related approaches. The experimental results show the superior performance of the proposed approaches in searching the feature space for optimal feature combinations.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectRough setsen_US
dc.subjectHybrid computer simulationen_US
dc.subjectComputer algorithmsen_US
dc.subject.lcshMathematical optimization
dc.subject.lcshSwarm intelligence
dc.titleHybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selectionen_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
item.grantfulltextopen-
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item.languageiso639-1other-
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