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Cost-sensitive online learning for control chart pattern recognition

delete2026-04-23
delete0
PRE
AI
P
Paul Okafor
T
Talayeh Razzaghi *
DOI：10.1016/j.cie.2026.112030delete
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摘要

摘要

En 中文
• 敏感成本在线模型提高了CCPR任务中的异常模式检测。• 我们引入了CSPA和CSOGD，以适应具有类别特定惩罚的演变数据。• 我们展示了其在合成和真实数据集上优于标准模型的优势。• 我们比较了模型在检测不同控制图模式趋势时的性能。
Keyword:
Cost-sensitive learning
Online learning
Control chart pattern recognition
Abnormal pattern detection
Class-specific penalties

期刊

C
Computers & Industrial Engineering
IF:
6.5
论文数:
535
被引数:
0

机构

U
university of oklahoma
学者数:
1.0K
论文数: 447
被引数: 0
U
University of Oklahoma
学者数:
712
论文数: 399
被引数: 2.3W
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