Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition
Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors suc...
- Autores:
-
Neira Rodado, Dionicio
Nugent, Christopher
Cleland, Ian
Velasquez, Javier
Viloria, Amelec
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7755
- Acceso en línea:
- https://hdl.handle.net/11323/7755
https://repositorio.cuc.edu.co/
- Palabra clave:
- HAR
dataset quality
machine learning
multivariate analysis
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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| dc.title.spa.fl_str_mv |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| title |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| spellingShingle |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition HAR dataset quality machine learning multivariate analysis |
| title_short |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| title_full |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| title_fullStr |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| title_full_unstemmed |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| title_sort |
Evaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognition |
| dc.creator.fl_str_mv |
Neira Rodado, Dionicio Nugent, Christopher Cleland, Ian Velasquez, Javier Viloria, Amelec |
| dc.contributor.author.spa.fl_str_mv |
Neira Rodado, Dionicio Nugent, Christopher Cleland, Ian Velasquez, Javier Viloria, Amelec |
| dc.subject.spa.fl_str_mv |
HAR dataset quality machine learning multivariate analysis |
| topic |
HAR dataset quality machine learning multivariate analysis |
| description |
Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2021-01-22T23:41:36Z |
| dc.date.available.none.fl_str_mv |
2021-01-22T23:41:36Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
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https://hdl.handle.net/11323/7755 |
| dc.identifier.doi.spa.fl_str_mv |
10.3390/s20071858 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/7755 https://repositorio.cuc.edu.co/ |
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10.3390/s20071858 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Prins J., Mader D. Multivariate Control Charts for Grouped and Individual Observations. Manuf. Syst. 2007:37–41. doi: 10.1080/08982119708919108 Fallon A., Spada C. Detection and accommodation of outliers. Environ. Sampl. Monit. Primer. 1997;6:217–230. Mahmoud S., Lotfi A., Langensiepen C. User activities outliers detection; Integration of statistical and computational intelligence techniques. Comput. Intell. 2016;32:49–71. doi: 10.1111/coin.12045. Aparisi F., Carlos J., Díaz G. Aumento de la potencia del gráfico de control multivariante T 2 de Hotelling utilizando señales adicionales de falta de control. Estadística Española. 2001;43:171–188. Bauder R.A., Khoshgoftaar T.M. Multivariate anomaly detection in medicare using model residuals and probabilistic programming; Proceedings of the FLAIRS 2017—30th International Florida Artificial Intelligence Research Society Conference; Marco Island, FL, USA. 22–24 May 2017; pp. 417–422. |
| dc.rights.spa.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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http://purl.org/coar/access_right/c_abf2 |
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Corporación Universidad de la Costa |
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Sensors (Basel) |
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Corporación Universidad de la Costa |
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https://pubmed.ncbi.nlm.nih.gov/32230844/ |
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Neira Rodado, Dionicio3be1ac9c504a1282ad14d2dee803adcfNugent, Christophera4db7dba9c111f48b12e074a1d1f7e31Cleland, Ian52c86c94b09dc567ee2901bc5b1c5b7dVelasquez, Javierf43cf330f7b0be6da526ab1c383eedcdViloria, Amelecfc29d54ed3c7d39e34b3d61c512ace8f2021-01-22T23:41:36Z2021-01-22T23:41:36Z2020https://hdl.handle.net/11323/775510.3390/s20071858Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sensors (Basel)https://pubmed.ncbi.nlm.nih.gov/32230844/HARdataset qualitymachine learningmultivariate analysisEvaluating the impact of a two-stage multivariate data cleansing approach to improve to the performance of machine learning classifiers: a case study in human activity recognitionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPrins J., Mader D. Multivariate Control Charts for Grouped and Individual Observations. Manuf. Syst. 2007:37–41. doi: 10.1080/08982119708919108Fallon A., Spada C. Detection and accommodation of outliers. Environ. Sampl. Monit. Primer. 1997;6:217–230.Mahmoud S., Lotfi A., Langensiepen C. User activities outliers detection; Integration of statistical and computational intelligence techniques. Comput. Intell. 2016;32:49–71. doi: 10.1111/coin.12045.Aparisi F., Carlos J., Díaz G. Aumento de la potencia del gráfico de control multivariante T 2 de Hotelling utilizando señales adicionales de falta de control. Estadística Española. 2001;43:171–188.Bauder R.A., Khoshgoftaar T.M. 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