Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
Management Science
Neural network ensembles: evaluation of aggregation algorithms
Artificial Intelligence
A hybrid approach for efficient ensembles
Decision Support Systems
Customer-adapted coupon targeting using feature selection
Expert Systems with Applications: An International Journal
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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.