Ensemble learning for customers targeting

Abstract

Customer targeting, which aims to identify and profile the households that are most likely to purchase a particular product or service, is one of the key problems in database marketing. In this paper, we propose an ensemble learning approach to address this problem. Our main idea is to construct different learning hypothesis by random sampling and feature selection. The advantage of the proposed approach for customers targeting is two-folded. First, the uncertainty and instability of single learning method is decreased. Second, the impact of class imbalance on learning bias is reduced. In the empirical study, logistic regression is employed as the basic learning method. The experimental result on a real-world dataset shows that our approach could achieve promising targeting accuracy with time parsimony. © 2011 Springer-Verlag.