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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