Cost-Restricted Feature Selection for Data Acquisition

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.

Automated summary

This study focuses on the problem of feature selection in customer data acquisition, considering different acquisition costs. The researchers formulate the problem as nonlinear discrete optimization problems to minimize prediction errors within a budget constraint. They derive analytical properties of the solutions, propose a computational procedure, provide an intuitive interpretation of the feature selection criteria, and discuss the managerial implications. Experimental results demonstrate the effectiveness of their approach.