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

When acquiring consumer data for marketing or new business initiatives, it is crucial to determine which attributes or features of potential customers should be obtained. This study focuses on a novel feature selection problem in the context of customer data acquisition, where different features come with different acquisition costs. The problem of feature selection is examined for linear regression and logistic regression. By formulating the problem as nonlinear discrete optimization problems, we aim to minimize prediction errors under a budget constraint. Analytical properties of the solutions are derived, and a computational procedure is developed to solve the problems. Additionally, the intuitive interpretation of the feature selection criteria is provided, and the managerial implications of the solution approach are discussed. Experimental results demonstrate the effectiveness of our approach.