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A Novel Feature Selection Approach Based on FODPSO and SVM

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Benediktsson, Jon Atli
dc.contributor.author Ghamisi, Pedram
dc.contributor.author Couceiro, Micael S.
dc.date.accessioned 2016-10-04T15:28:21Z
dc.date.available 2016-10-04T15:28:21Z
dc.date.issued 2015-05
dc.identifier.citation P. Ghamisi, M. S. Couceiro and J. A. Benediktsson, (2015) A Novel Feature Selection Approach Based on FODPSO and SVM, IEEE Transactions on Geoscience and Remote Sensing,53,(5), 2935-2947. doi: 10.1109/TGRS.2014.2367010
dc.identifier.issn 0196-2892
dc.identifier.issn 1558-0644 (e-ISSN)
dc.identifier.uri https://hdl.handle.net/20.500.11815/139
dc.description.abstract A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.
dc.description.sponsorship IEEE Geoscience and Remote Sensing Society
dc.format.extent 2935-2947
dc.language.iso en
dc.publisher IEEE
dc.relation.ispartofseries IEEE Transactions on Geoscience and Remote Sensing;53(5)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Support vector machines
dc.subject Data reduction
dc.subject Hyperspectral imaging
dc.title A Novel Feature Selection Approach Based on FODPSO and SVM
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 Ritrýnt tímarit
dc.description.version Peer Reviewed
dc.identifier.journal IEEE Transactions on Geoscience and Remote Sensing
dc.identifier.doi 10.1109/TGRS.2014.2367010
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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