{"id":1875,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=1875"},"modified":"2021-07-15T09:33:08","modified_gmt":"2021-07-15T07:33:08","slug":"comparison-of-feature-selection-algorithms-for-prediction-of-quality-characteristics","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/quality-assurance-testing\/comparison-of-feature-selection-algorithms-for-prediction-of-quality-characteristics\/","title":{"rendered":"Comparison of feature selection algorithms for prediction of quality characteristics"},"content":{"rendered":"\n

by Simon Cramer, Daniel Buschmann, Robert H. Schmitt<\/em><\/em> (Germany)<\/em><\/p>\n\n\n\n

<\/p>\n\n\n\n

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

Data acquired during production processes often contain redundant and irrelevant features. Thus, to predict quality characteristics from process data, precise feature extraction is essential to sustain a low prediction error and to limit the computational complexity of the deployed machine learning models. Hence, we compare two feature extraction methods: principal component analysis and an autoencoder. Based on an industrial use case, we highlight the advantages of the methods and provide guidance in creating an automated data analysis pipeline for the prediction of quality characteristics. This pipeline is fundamental for other predictive quality applications such as smart experts. Our results favor the principal component analysis for feature extraction, even though it is less expressive than autoencoders.<\/p>\n\n\n\n

Keywords<\/strong>: Predictive quality, Feature extraction, Autoencoder<\/p>\n\n\n\n

<\/div>\n\n\n\n

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