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Label Dependent Evolutionary Feature Weighting for Remote Sensing Data

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dc.creator Mateos García, Daniel es
dc.creator García Gutiérrez, Jorge es
dc.creator Riquelme Santos, José Cristóbal es
dc.date.accessioned 2016-04-27T10:50:41Z
dc.date.available 2016-04-27T10:50:41Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/11441/40523
dc.description.abstract Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and land cover (LULC) maps. Evolutive computation has often been used to obtain feature weighting in order to improve the results of the NN. In this paper, a new algorithm based on evolutionary computation which has been called Label Dependent Feature Weighting (LDFW) is proposed. The LDFW method transforms the feature space assigning different weights to every feature depending on each class. This multilevel feature weighting algorithm is tested on remote sensing data from fusion of sensors (LIDAR and orthophotography). The results show an improvement on the NN and resemble the results obtained with a neural network which is the best classifier for the study area. es
dc.format application/pdf es
dc.language.iso eng es
dc.relation.ispartof Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, Volume 6077, pp 272-279 es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Remote sensing es
dc.subject Feature weighting es
dc.subject Evolutionary computation es
dc.subject Label dependence es
dc.title Label Dependent Evolutionary Feature Weighting for Remote Sensing Data es
dc.type info:eu-repo/semantics/bookPart es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.identifier.doi http://dx.doi.org/10.1007/978-3-642-13803-4_34 es
idus.format.extent 7 es
dc.identifier.idus https://idus.us.es/xmlui/handle/11441/40523
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