Evaluation of four classifiers as cost function for indoor location systems

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dc.creatorRamón Brena Pinero
dc.date2014
dc.date.accessioned2018-10-18T21:21:52Z
dc.date.available2018-10-18T21:21:52Z
dc.descriptionIn our previous research work, we proposed a methodology that uses magnetic-field and multivariate methods to estimate user location in an indoor environment. In this paper, we propose the use of this methodology to evaluate the performance of four different classification algorithms: Random Forest, Nearest Centroid, K Nearest Neighbors and Artificial Neural Networks; each classifier will be considered as a cost function of a genetic algorithm (GA) used in the feature selection process task of the methodology. The motivation to evaluate the algorithms of classification was that several ILSs use a classification algorithm in order to estimate the location of the user, but the classifiers performance vary from application to application. In order to evaluate the performance of each classification algorithm, the following issues were considered: (1) the time of the training phase to obtain the final classification algorithm; (2) the number of features needed for getting the model; (3) the type of the features from the final model; and (4) the sensitivity and specificity of the model. Our results indicate that Nearest centroid is the classfier algorithm that is best suited to be implemented in an end-user application given the obtained results on the evaluated criteria for the indoor location system (ILS). © 2014 Published by Elsevier B.V.
dc.identifier.doi10.1016/j.procs.2014.05.447
dc.identifier.endpage460
dc.identifier.issn18770509
dc.identifier.startpage453
dc.identifier.urihttp://hdl.handle.net/11285/630417
dc.identifier.volume32
dc.languageeng
dc.publisherElsevier
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84902659269&doi=10.1016%2fj.procs.2014.05.447&partnerID=40&md5=457a982b1489dd56bbe8cd345b6d5415
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceProcedia Computer Science
dc.subjectAlgorithms
dc.subjectCost functions
dc.subjectDecision trees
dc.subjectEnergy conservation
dc.subjectGenetic algorithms
dc.subjectIndoor positioning systems
dc.subjectLocation
dc.subjectMagnetic levitation vehicles
dc.subjectMotion compensation
dc.subjectNearest neighbor search
dc.subjectNeural networks
dc.subjectClassifier algorithms
dc.subjectIndoor locations
dc.subjectK-nearest neighbors
dc.subjectMultivariate methods
dc.subjectNearest Centroid
dc.subjectFunction evaluation
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleEvaluation of four classifiers as cost function for indoor location systems
dc.typeConferencia
refterms.dateFOA2018-10-18T21:21:52Z

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