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

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


Titill: Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization
Höfundur: Ghamisi, Pedram
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Útgáfa: 2015-02
Tungumál: Enska
Umfang: 309-313
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
ISSN: 1545-598x
DOI: 10.1109/LGRS.2014.2337320
Efnisorð: Accuracy; Feature extraction; Genetic algorithms; Roads; Sociology; Support vector machines; Training
URI: https://hdl.handle.net/20.500.11815/63

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Tilvitnun:

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

Útdráttur:

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.

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