Skip to main content

Research Repository

Advanced Search

Ensemble learning with dynamic weighting for response modeling in direct marketing

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

Ensemble learning with dynamic weighting for response modeling in direct marketing Thumbnail


Xin Zhang

Yalan Zhou

Yu Wang


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.

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
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 64
Article Number 101371
Public URL


You might also like

Downloadable Citations