Active learning refers to the mechanism of querying users to accomplish a classification task in machine learning or a conjoint analysis in econometrics with minimum cost. Classification and conjoint analysis have been introduced to design research to automate design feasibility checking and to construct marketing demand models, respectively. In this paper, we review active learning algorithms from computer and marketing science, and establish the mathematical commonality between the two approaches. We compare empirically the performance of active learning and static D-optimal design on simulated classification and conjoint analysis test problems with labelling noise. Results show that active learning outperforms D-optimal design when query size is large or noise is small.
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ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 12–15, 2012
Chicago, Illinois, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-4502-8
PROCEEDINGS PAPER
On the Use of Active Learning in Engineering Design Available to Purchase
Panos Y. Papalambros
Panos Y. Papalambros
University of Michigan, Ann Arbor, MI
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Yi Ren
University of Michigan, Ann Arbor, MI
Panos Y. Papalambros
University of Michigan, Ann Arbor, MI
Paper No:
DETC2012-70624, pp. 89-98; 10 pages
Published Online:
September 9, 2013
Citation
Ren, Y, & Papalambros, PY. "On the Use of Active Learning in Engineering Design." Proceedings of the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3: 38th Design Automation Conference, Parts A and B. Chicago, Illinois, USA. August 12–15, 2012. pp. 89-98. ASME. https://doi.org/10.1115/DETC2012-70624
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