正确性
趋同(经济学)
样品(材料)
过程(计算)
计算机科学
断层(地质)
数据建模
数据挖掘
人工智能
机器学习
算法
地质学
地震学
数据库
化学
色谱法
经济
经济增长
操作系统
作者
Zhijun Ren,Jinchen Ji,Yongsheng Zhu,Ke Feng
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:73: 1-20
被引量:1
标识
DOI:10.1109/tim.2023.3343775
摘要
In solving the data imbalance problem, most of the existing studies ignored the effect of the number of samples on the diagnostic performance of intelligent fault diagnostic models. When the number of minority samples is small, the data imbalance situation becomes a compound problem of data imbalance and small sample, making it challenging to develop effective methods and identify core issues. This article aims to investigate the effects of the compound problem on intelligent diagnostic models by using the number of samples and the number of majority classes as indicators to assess the deterioration behavior of diagnostic models. Multiple datasets are systematically studied to explore three categories of influences: the influence of the number of minority samples on the model's learning ability, the influence of the relative size of the numbers of majority samples and minority samples on the model's convergence speed, and the influence of the relative size of the numbers of majority samples and minority samples on the model's convergence correctness. The exacerbation of imbalance by an increase in the number of majority classes further deepens the influence of imbalance on the convergence of the model. Furthermore, this article explores the degradation mechanism of intelligent diagnostic models. Finally, a semiquantitative deterioration process is proposed to guide future studies on imbalanced learning in experimental design and method evaluation.
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