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) |