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Ensemble learning as approach for pipeline condition assessment

Author
Camacho, J.; Ruiz, M.; Villamizar, R.; Mujica, L.E.; Moreno, G.
Type of activity
Journal article
Journal
Journal of physics: conference series
Date of publication
2017
Volume
842
Number
1
First page
1
Last page
11
DOI
https://doi.org/10.1088/1742-6596/842/1/012019 Open in new window
Repository
http://hdl.handle.net/2117/107225 Open in new window
Abstract
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...
Citation
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.
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

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