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- W4296250489 abstract "Developments in Machine Learning (ML) in the last years resulted in taking as granted their usage and their necessity clear in areas such as manufacturing and quality control. Such areas include case specific requirements and restrictions that require the human expert’s knowledge and effort to apply the ML algorithms efficiently. This paper proposes a framework architecture that utilizes Automated Machine Learning (AutoML) to minimize human intervention while constructing and maintaining ML models for quality control. The data analyst gives the setting for multiple configurations while designing predictive quality models which are automatically optimized and maintained. Moreover, experiments are conducted to test the framework in both the performance of the prediction models and the time needed to construct the models." @default.
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- W4296250489 date "2022-01-01" @default.
- W4296250489 modified "2023-09-27" @default.
- W4296250489 title "An Automated Machine Learning Framework for Predictive Analytics in Quality Control" @default.
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- W4296250489 doi "https://doi.org/10.1007/978-3-031-16407-1_3" @default.
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