Determination of the most relevant features to improve the performance of RF classifier in human activity recognition

dc.creatorJiménez-Gómez, Geovanna
dc.creatorNavarro-Escorcia, Daniela
dc.creatorNeira Rodado, Dionicio
dc.creatorCleland, Ian
dc.date2021-11-08T13:11:42Z
dc.date2021-11-08T13:11:42Z
dc.date2021-09-17
dc.date2022-09-17
dc.date.accessioned2023-10-03T20:02:07Z
dc.date.available2023-10-03T20:02:07Z
dc.identifier978-303084339-7
dc.identifierhttps://hdl.handle.net/11323/8843
dc.identifier10.1007/978-3-030-84340-3_3
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174035
dc.descriptionThe impact that neurodegenerative diseases have in our society, have made human activity recognition (HAR) arise as a relevant field of study. The quality of life of people with such conditions, can be significantly improved with the outcomes of the projects within this area. The application of machine learning techniques on data from low level sensors such as accelerometers is the base of HAR. To improve the performance of these classifiers, it is necessary to carry out an adequate training process. To improve the training process, an analysis of the different features used in literature to tackle these problems was performed on datasets constructed with students performing 18 different activities of daily living. The outcome of the process shows that an adequate selection of features improves the performance of Random Forest from 94.6% to 97.2%. It was also found that 78 features explain 80% of the variability.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpringer International Publishing
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourceLecture Notes in Computer Science
dc.sourcehttps://www.springerprofessional.de/en/determination-of-the-most-relevant-features-to-improve-the-perfo/19669904
dc.subjectHAR
dc.subjectMachine learning
dc.subjectFeature selection
dc.subjectRF classifier
dc.titleDetermination of the most relevant features to improve the performance of RF classifier in human activity recognition
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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