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Automatic classification of respiratory patterns involving missing data imputation techniques

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http://hdl.handle.net/2183/18101
Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España
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Title
Automatic classification of respiratory patterns involving missing data imputation techniques
Author(s)
Hernández-Pereira, Elena
Álvarez-Estévez, Diego
Moret-Bonillo, Vicente
Date
2015-10
Citation
Elena M. Hernández-Pereira, Diego Álvarez-Estévez, Vicente Moret-Bonillo, Automatic classification of respiratory patterns involving missing data imputation techniques, Biosystems Engineering 138 (2015), pp. 65–76
Abstract
[Abstract] A comparative study of the respiratory pattern classification task, involving five missing data imputation techniques and several machine learning algorithms is presented in this paper. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that in general, the Self-Organising Map imputation method allows non-tree based classifiers to achieve improvements over the rest of the imputation methods in terms of the classification accuracy, and that the Feedforward neural network and the Random Forest classifiers offer the best performance regardless of the imputation method used. The improvements in terms of accuracy over the previous work of the authors are limited but the Feed Forward neural network model achieves promising results.
Keywords
Respiratory pattern classification
Missing data imputation
Machine learning
 
Editor version
http://dx.doi.org/10.1016/j.biosystemseng.2015.06.011
Rights
Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
1537-5110
1537-5129
 

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