Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence

Addressing Noise and Skewness in Interpretable Health-Condition Assessment by Learning Model Confidence

Assessing the health condition has a wide range of applications in healthcare, military, aerospace, and industrial fields. Nevertheless, traditional feature-engineered techniques involve manual feature extraction, which are too cumbersome to adapt to the changes caused by the development of sensor n...

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Bibliographic Details
Main Authors: Yuxi Zhou, Shenda Hong, Junyuan Shang, Meng Wu, Qingyun Wang, Hongyan Li, Junqing Xie
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Sensors
Subjects:
noise
class skewness
model interpretability
deep learning
health-condition assessment
Online Access:https://www.mdpi.com/1424-8220/20/24/7307

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