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Stream-based active learning with linear models
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; .ORCID iD: 0000-0003-4222-9631
Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
2022 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 254, article id 109664Article in journal (Refereed) Published
Abstract [en]

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 254, article id 109664
Keywords [en]
Active learning, Unlabeled data, Optimal experimental design, Linear regression, Soft sensor
National Category
Information Systems Reliability and Maintenance
Research subject
Quality technology and logistics
Identifiers
URN: urn:nbn:se:ltu:diva-92922DOI: 10.1016/j.knosys.2022.109664ISI: 000861089400008Scopus ID: 2-s2.0-85138441801OAI: oai:DiVA.org:ltu-92922DiVA, id: diva2:1696021
Note

Validerad;2022;Nivå 2;2022-09-15 (hanlid)

Available from: 2022-09-15 Created: 2022-09-15 Last updated: 2022-11-09Bibliographically approved

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Kulahci, Murat

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