Abstract
Nowadays, the implementation of embedded systems with sensors for massive data collection has become widely used for their flexibility and improvement in decision making. However, this process can be affected by errors in reading, attrition of systems, among others. For this, a selection approach of supervised algorithms with a prototypes selection criterion is presented, which allows an adequate embedded system performance. To do that a quality measure was established which compromises between the data reduction of the training set, algorithm processing time and the classification performance. As a result, it was determined that the algorithm for the data selection is Condensed Nearest Neighbors (CNN) and the classification algorithm is k-Nearest Neighbour (k-NN).
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Acknowledgment
The authors acknowledge to the research project “Desarrollo de una metodología de visualización interactiva y eficaz de información en Big Data” supported by Agreement No. 180 November 1st, 2016 by VIPRI from Universidad de Nariño.
This work is supported by the “Smart Data Analysis Systems - SDAS” group (http://sdas-group.com).
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Godoy-Trujillo, P.E., Rosero-Montalvo, P.D., Suárez-Zambrano, L.E., Peluffo-Ordoñez, D.H., Revelo-Fuelagán, E.J. (2020). A New Approach to Supervised Data Analysis in Embedded Systems Environments: A Case Study. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_29
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