Automatic classification of respiratory patterns involving missing data imputation techniques. (October 2015)

• Record Type: Journal Article
• Authors: Hernández-Pereira, Elena M.; Álvarez-Estévez, Diego; Moret-Bonillo, Vicente
• Is Part Of: Biosystems engineering. Volume 138(2015:Oct.)
• Page Start: 65
• Page End: 76
• Publication Date: 2015-10
• Subjects: Respiratory pattern classification -- Missing data imputation -- Machine learning; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2015.06.011
• Languages: English
• ISSNs: 1537-5110

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. Highlights: A comparative study over respiratory pattern classification in the field of SAHS. Several missing data imputation techniques and machine learning algorithms were tried. The inputs are features extracted from respiratory and neurophysiological signals. The goal is to improve classification and imputation results of a previous work. FNN and Ensemble of Trees offer the best performance with any imputation method.