@inproceedings{6dea5a594990428a87d9f21578abb13d,
title = "On the use of active learning in engineering design",
abstract = "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.",
author = "Yi Ren and Papalambros, {Panos Y.}",
year = "2012",
doi = "10.1115/DETC2012-70624",
language = "English (US)",
isbn = "9780791845028",
series = "Proceedings of the ASME Design Engineering Technical Conference",
number = "PARTS A AND B",
pages = "89--98",
booktitle = "ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012",
edition = "PARTS A AND B",
note = "ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012 ; Conference date: 12-08-2012 Through 12-08-2012",
}