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Cost-sensitive online learning for control chart pattern recognition
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DOI：10.1016/j.cie.2026.112030.png)
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Abstract
En 中文
• Cost-sensitive online models improve abnormal pattern detection in CCPR tasks. • We introduced CSPA and CSOGD to adapt to evolving data with class-specific penalties. • We showed its advantage over standard models on synthetic and real datasets. • We compared the models’ performance to detect different control chart pattern trends.
Keywords:
Cost-sensitive learning
Online learning
Control chart pattern recognition
Abnormal pattern detection
Class-specific penalties
Journal
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6.5
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565
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0
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