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Robust online active learning
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.ORCID iD: 0000-0003-1628-4725
2024 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 40, no 1, p. 277-296Article in journal (Refereed) Published
Abstract [en]

In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high-volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024. Vol. 40, no 1, p. 277-296
Keywords [en]
active learning, data stream, optimal experimental design, outliers, robust regression, unlabeled data
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Quality Technology and Logistics
Identifiers
URN: urn:nbn:se:ltu:diva-98586DOI: 10.1002/qre.3392ISI: 001002100700001Scopus ID: 2-s2.0-85161536751OAI: oai:DiVA.org:ltu-98586DiVA, id: diva2:1770530
Note

Validerad;2024;Nivå 2;2024-02-14 (sofila);

Funder: DTU Strategic Alliances Fund;

Full text license: CC BY 4.0

Available from: 2023-06-19 Created: 2023-06-19 Last updated: 2024-02-14Bibliographically approved

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Kulahci, Murat

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