{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:33:22Z","timestamp":1773246802025,"version":"3.50.1"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            An Industrial Cyber-physical System (ICPS) provides a digital foundation for data-driven decision-making by artificial intelligence (AI) models. However, the poor data quality (e.g., inconsistent distribution, imbalanced classes) of high-speed, large-volume data streams poses significant challenges to the online deployment of offline-trained AI models. As an alternative, updating AI models online based on streaming data enables continuous improvement and resilient modeling performance. However, for a supervised learning model (i.e., a base learner), it is labor-intensive to annotate all streaming samples to update the model. Hence, a data acquisition method is needed to select the data for annotation to ensure data quality while saving annotation efforts. In the literature, active learning methods have been proposed to acquire informative samples. Different acquisition criteria were developed for exploration of under-represented regions in the input variable space or exploitation of the well-represented regions for optimal estimation of base learners. However, it remains a challenge to balance the exploration-exploitation trade-off under different online annotation scenarios. On the other hand, an acquisition criterion learned by AI adapts itself to a scenario dynamically, but the ambiguous consideration of the trade-off limits its performance in frequently changing manufacturing contexts. To overcome these limitations, we propose an ensemble active learning method by contextual bandits (\n            <jats:sc>CbeAL<\/jats:sc>\n            ).\n            <jats:sc>CbeAL<\/jats:sc>\n            incorporates a set of active learning agents (i.e., acquisition criteria) explicitly designed for exploration or exploitation by a weighted combination of their acquisition decisions. The weight of each agent will be dynamically adjusted based on the usefulness of its decisions to improve the performance of the base learner. With adaptive and explicit consideration of both objectives,\n            <jats:sc>CbeAL<\/jats:sc>\n            efficiently guides the data acquisition process by selecting informative samples to reduce the human annotation efforts. Furthermore, we characterize the exploration and exploitation capability of the proposed agents theoretically. The evaluation results in a numerical simulation study and a real case study demonstrates the effectiveness and efficiency of\n            <jats:sc>CbeAL<\/jats:sc>\n            in manufacturing process modeling of the ICPS.\n          <\/jats:p>","DOI":"10.1145\/3627821","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T21:38:02Z","timestamp":1698269882000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4726-3946","authenticated-orcid":false,"given":"Yingyan","family":"Zeng","sequence":"first","affiliation":[{"name":"Grado Department of Industrial and Systems Engineering, Virginia Tech, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1870-5290","authenticated-orcid":false,"given":"Xiaoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, University at Buffalo, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3847-4538","authenticated-orcid":false,"given":"Ran","family":"Jin","sequence":"additional","affiliation":[{"name":"Grado Department of Industrial and Systems Engineering, Virginia Tech, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3351228"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/0951192X.2020.1747642"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539701398375"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3164539"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/1768841.1768848"},{"key":"e_1_3_2_7_2","first-page":"255","article-title":"Online choice of active learning algorithms","volume":"5","author":"Baram Yoram","year":"2004","unstructured":"Yoram Baram, Ran El Yaniv, and Kobi Luz. 2004. 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