{"id":3279,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3279"},"modified":"2022-07-26T13:07:28","modified_gmt":"2022-07-26T11:07:28","slug":"on-the-importance-of-domain-expertise-in-feature-engineering-for-predictive-product-quality-in-production","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/on-the-importance-of-domain-expertise-in-feature-engineering-for-predictive-product-quality-in-production\/","title":{"rendered":"On the importance of domain expertise in feature engineering for predictive product quality in production"},"content":{"rendered":"\n

by Hendrik Mende, Maik Frye, Paul-Alexander Vogel, Saksham Kiroriwal, Robert H. Schmitt, Thomas Bergs (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

Machine Learning (ML) offers significant potential for quality management in production with predictive analytics. Key aspects to building ML models are the selection and engineering of features from data. They allow the usage of relevant data for training ML models. Using the right features consequently improves the quality of the ML models. However, feature engineering requires knowledge of the data, data preprocessing techniques, algorithms, the domain, and use case. Hence, automatic feature engineering tools have become popular. In this paper, we investigate how domain experts and automatic tools compare for engineering features based on a time series dataset from production.<\/p>\n\n\n\n

Keywords<\/strong>: Machine Learning; Domain expertise; Feature selection; Feature engineering; Product quality; Production<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n