Ensemble learning with dynamic weighting for response modeling in direct marketing

Zhang, Xin; Zhou, Yalan; Lin, Zhibin; Wang, Yu

Abstract

Response modeling, a key to successful direct marketing, has become increasingly prevalent in recent years. However, it practically suffers from the difficulty of class imbalance, i.e., the number of responding (target) customers is often much smaller than that of the non-responding customers. This issue would result in a response model that is biased to the majority class, leading to the low prediction accuracy on the responding customers. In this study, we develop an Ensemble Learning with Dynamic Weighting (ELDW) approach to address the above problem. The proposed ELDW includes two stages. In the first stage, all the minority class instances are combined with different majority class instances to form a number of training subsets, and a base classifiers is trained in each subset. In the second stage, the results of the base classifiers are dynamically integrated, in which two factors are considered. The first factor is the cross entropy of neighbors in each subset, and the second factor is the feature similarity to the minority class instances. In order to evaluate the performance of ELDW, we conduct experimental studies on 10 imbalanced benchmark datasets. The results show that compared with other state-of-the-art imbalance classification algorithms, ELDW achieves higher accuracy on the minority class. Last, we apply the ELDW to a direct marketing activity of an insurance company to identify the target customers under a limited budget.

Citation

Zhang, X., Zhou, Y., Lin, Z., & Wang, Y. (2024). Ensemble learning with dynamic weighting for response modeling in direct marketing. Electronic Commerce Research and Applications, 64, Article 101371. https://doi.org/10.1016/j.elerap.2024.101371

• Journal Article Type: Article
• Acceptance Date: Feb 7, 2024
• Online Publication Date: Feb 10, 2024
• Publication Date: 2024-03
• Deposit Date: Feb 22, 2024
• Publicly Available Date: Feb 22, 2024
• Journal: Electronic Commerce Research and Applications
• Print ISSN: 1567-4223
• Electronic ISSN: 1873-7846
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 64
• Article Number: 101371
• DOI: https://doi.org/10.1016/j.elerap.2024.101371
• Public URL: https://durham-repository.worktribe.com/output/2272162