Classification of severity of trachea stenosis from EEG signals using ordinal decision-tree based algorithms and ensemble-based ordinal and non-ordinal algorithms
作者：
Highlights：
• EEG signals are used for the identification of airway obstruction levels.
• A new ordinal random-forest approach yields better results than other classifiers.
• An ensemble approach based on ordinal and non-ordinal classifiers is proposed.
• The ensemble approach outperforms each individual classifier on most measures.
• The technique shows promise as a supplemental test for grading airway obstruction.
摘要
•EEG signals are used for the identification of airway obstruction levels.•A new ordinal random-forest approach yields better results than other classifiers.•An ensemble approach based on ordinal and non-ordinal classifiers is proposed.•The ensemble approach outperforms each individual classifier on most measures.•The technique shows promise as a supplemental test for grading airway obstruction.
论文关键词：Airway obstruction,Electroencephalogram,Ensemble learning,Machine learning,Ordinal classification
论文评审过程：Received 11 March 2020, Revised 24 December 2020, Accepted 9 February 2021, Available online 16 February 2021, Version of Record 26 February 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114707
