Cost-Restricted Feature Selection for Data Acquisition

Xiaoping Liu (), Xiao-Bai Li () and Sumit Sarkar ()
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Xiaoping Liu: D’Amore-McKim School of Business, Northeastern University, Boston, Massachusetts 02115
Xiao-Bai Li: Department of Operations and Information Systems, Manning School of Business, University of Massachusetts Lowell, Lowell, Massachusetts 01854
Sumit Sarkar: Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Management Science, 2023, vol. 69, issue 7, 3976-3992

Abstract: When acquiring consumer data for marketing or new business initiatives, it is important to decide what attributes or features of potential customers should be acquired. We study a new feature selection problem in the context of customer data acquisition in which different features have different acquisition costs. This feature selection problem is studied for linear regression and logistic regression. We formulate the feature selection and acquisition problems as nonlinear discrete optimization problems that minimize prediction errors subject to a budget constraint. We derive the analytical properties of the solutions for the problems, develop a computational procedure for solving the problems, provide an intuitive interpretation for the feature selection criteria, and discuss managerial implications of the solution approach. The results of the experimental study demonstrate the effectiveness of our approach.

Keywords: data acquisition; feature selection; Lasso; linear regression; logistic regression (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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