Título: A New Approach to Supervised Data Analysis in Embedded Systems Environments: A Case Study

Autor(es): GODOY TRUJILLO PAMELA ESTEFANÍA, ROSERO MONTALVO PAUL DAVID, SUAREZ ZAMBRANO LUIS EDILBERTO

Fecha de publicación: 04-jul-2020

Resumen: 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)

Palabras clave: Data analysis Sensor data Embedded systems

DOI: https://doi.org/10.1007/978-3-030-52249-0_29

ISSN: 21945357

Tipo publicación: Artículo