Multi-Phases and Various Feature Extraction and Selection Methodology for Ensemble Gradient Boosting in Estimating Respiratory Rate-Reference-Cited by-同舟云学术

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Multi-Phases and Various Feature Extraction and Selection Methodology for Ensemble Gradient Boosting in Estimating Respiratory Rate

Published:2020 Issue: Volume:8 Page:125648-125658
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lee Soojeong¹^ORCID,Son Chang-Hwan²^ORCID,Albertini Marcelo K.³^ORCID,Fernandes Henrique C.³^ORCID

Affiliation:

1. Department of Computer Engineering, Sejong University, Seoul, South Korea

2. Department of Software Convergence Engineering, Kunsan National University, Gunsan, South Korea

3. School of Computer Science, Federal University of Uberlandia, Uberlandia, Brazil

Funder

National Research Foundation of Korea (NRF) grant

Korea government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09133563.pdf?arnumber=9133563

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automatic Features Extraction Integrated With Exact Gaussian Process for Respiratory Rate and Uncertainty Estimations;IEEE Access;2023

2. Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations;Sensors;2022-11-01

3. Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm;Applied Sciences;2022-08-21

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