Combining Representation Learning and Active Learning for Applications in Process Manufacturing.

Autor:	Dathatri, Ashwini Gonibedu, Strem, Nika, Klöpper, Benjamin
Předmět:	ACTIVE learning MANUFACTURING processes TIME series analysis MACHINE learning SECURE Sockets Layer (Computer network protocol) DEEP learning SUPERVISED learning
Zdroj:	Chemie Ingenieur Technik (CIT); Jul2023, Vol. 95 Issue 7, p1018-1027, 10p
Abstrakt:	Multivariate time series classification (TSC) is an important data‐driven modeling task in process industries. A common challenge of TSC in the process industry is the unavailability of labels. Active learning (AL) tackles this problem by incrementally obtaining as few labeled instances as possible by querying an oracle. From our initial experience of building an AL application we derive requirements on the machine learning approach in the context of such an interactive application. To meet the requirements, we combine AL with representation learning (RL): a model learns a latent space representation of unlabeled data, which is then used to train a classification model on the labeled data. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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