Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis
Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis
Jingyao Wu,Zhibin Zhao,2 Authors,Xuefeng Chen
2021 · DOI: 10.1016/J.RESS.2021.107934
Reliability Engineering & System Safety · 63 Citations
TLDR
A model-agnostic framework towards class-imbalanced fault diagnosis requirement is proposed to systematically alleviate problems of minority categories overfitting and majority categories domination and outperformed various state-of-the-art methods.