dc.creator | García Gutiérrez, Jorge | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-07-14T08:21:39Z | |
dc.date.available | 2016-07-14T08:21:39Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | García Gutiérrez, J., Martínez Álvarez, F., Troncoso Lora, A. y Riquelme Santos, J.C. (2015). A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables. Neurocomputing, 167, 24-31. | |
dc.identifier.issn | 0925-2312 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43592 | |
dc.description.abstract | Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental
models in forest areas have been benefited by the use of LiDAR-derived information in the last years. A multiple linear
regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop
those models. MLR defines the relation between the set of field measurements and the statistics extracted from a
LiDAR flight. Machine learning has emerged as a suitable tool to improve classic stepwise MLR results on LiDAR.
Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches.
This paper presents a comparison between the classic MLR-based methodology and regression techniques in machine
learning (neural networks, support vector machines, nearest neighbour, ensembles such as random forests) with
special emphasis on regression trees. The selected techniques are applied to real LiDAR data from two areas in the
province of Lugo (Galizia, Spain). The results confirm that classic MLR is outperformed by machine learning techniques
and concretely, our experiments suggest that Support Vector Regression with Gaussian kernels statistically
outperforms the rest of the techniques. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN2011-28956-C02 | es |
dc.description.sponsorship | Junta de Andalucía P12- TIC-1728 | es |
dc.description.sponsorship | Universidad Pablo de Olavide APPB813097 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neurocomputing, 167, 24-31. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | LiDAR | es |
dc.subject | Machine learning | es |
dc.subject | regression | es |
dc.subject | Remote sensing | es |
dc.title | A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.accessRights | info: eu-repo/semantics/embargoAccess | |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2011-28956-C02 | es |
dc.relation.projectID | P12- TIC-1728 | es |
dc.relation.projectID | APPB813097 | es |
dc.date.embargoEndDate | 2017-11-05 | |
dc.identifier.doi | http://dx.doi.org/10.1016/j.neucom.2014.09.091 | es |
idus.format.extent | 8 | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 167 | es |
dc.publication.initialPage | 24 | es |
dc.publication.endPage | 31 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43592 | |