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Article

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

1
Department of Industrial Agroindustrial and Operations Management GIAO, Universidad de la Costa, Barranquilla 080002, Colombia
2
School of Computing, Ulster University, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 1858; https://doi.org/10.3390/s20071858
Received: 27 December 2019 / Revised: 12 March 2020 / Accepted: 13 March 2020 / Published: 27 March 2020
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
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%. View Full-Text
Keywords: multivariate analysis; HAR; machine learning; dataset quality multivariate analysis; HAR; machine learning; dataset quality
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MDPI and ACS Style

Neira-Rodado, D.; Nugent, C.; Cleland, I.; Velasquez, J.; Viloria, A. 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. Sensors 2020, 20, 1858. https://doi.org/10.3390/s20071858

AMA Style

Neira-Rodado D, Nugent C, Cleland I, Velasquez J, Viloria A. 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. Sensors. 2020; 20(7):1858. https://doi.org/10.3390/s20071858

Chicago/Turabian Style

Neira-Rodado, Dionicio, Chris Nugent, Ian Cleland, Javier Velasquez, and Amelec Viloria. 2020. "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" Sensors 20, no. 7: 1858. https://doi.org/10.3390/s20071858

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