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Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Ghamisi, Pedram
dc.contributor.author Benediktsson, Jon Atli
dc.date.accessioned 2016-08-17T07:15:45Z
dc.date.available 2016-08-17T07:15:45Z
dc.date.issued 2015-02
dc.identifier.citation P. Ghamisi and J. A. Benediktsson, "Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization," in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 309-313, Feb. 2015. Doi:10.1109/LGRS.2014.2337320
dc.identifier.issn 1545-598x
dc.identifier.uri https://hdl.handle.net/20.500.11815/63
dc.description.abstract A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users. Furthermore, the usefulness of the proposed method is also tested for road detection. Results confirm that the proposed method is capable of discriminating between road and background pixels and performs better than the other approaches used for comparison in terms of performance metrics.
dc.description.sponsorship Rannís; Rannsóknarnámssjóður / The Icelandic Research Fund for Graduate Students.
dc.format.extent 309-313
dc.language.iso en
dc.publisher IEEE Geoscience & Remote Sensing Society
dc.relation.ispartofseries IEEE Geoscience and Remote Sensing Letters;12(2)
dc.rights info:eu-repo/semantics/restrictedAccess
dc.subject Accuracy
dc.subject Feature extraction
dc.subject Genetic algorithms
dc.subject Roads
dc.subject Sociology
dc.subject Support vector machines
dc.subject Training
dc.title Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization
dc.type info:eu-repo/semantics/article
dcterms.license (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.description.version PostPrint
dc.identifier.journal IEEE Geoscience and Remote Sensing Letters
dc.identifier.doi 10.1109/LGRS.2014.2337320
dc.relation.url DOI: 10.1109/LGRS.2014.2337320
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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