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Ensemble learning as approach for pipeline condition assessment

Autor
Camacho-Navarro, J.; Ruiz, M.; Villamizar, R.; Mujica, L.E.; Moreno, G.
Tipus d'activitat
Article en revista
Revista
Journal of physics: conference series
Data de publicació
2017
Volum
842
Número
1
Pàgina inicial
1
Pàgina final
11
DOI
https://doi.org/10.1088/1742-6596/842/1/012019 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/107225 Obrir en finestra nova
Resum
The algorithms commonly used for damage condition monitoring present several drawbacks related to unbalanced data, optimal training requirements, low capability to manage feature diversity and low tolerance to errors. In this work, an approach based on ensemble learning is discussed as alternative to obtain more efficient diagnosis. The main advantage of ensemble learning is the use of several algorithms at the same time for a better proficiency. Thereby, combining simplest tree decision algorit...
Citació
Camacho-Navarro, J., Ruiz, M., Villamizar, R., Mujica, L.E., Moreno, G. Ensemble learning as approach for pipeline condition assessment. "Journal of physics: conference series", 2017, vol. 842, núm. 1, p. 1-11.
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