Ensemble learning with dynamic weighting for response modeling in direct marketing

作者全名："Zhang, Xin; Zhou, Yalan; Lin, Zhibin; Wang, Yu"

作者地址："[Zhang, Xin] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Dis, Key Lab Child Dev & Disorders, Dept Nursing,Childrens Hosp,Minist Educ,Chongqing, Chongqing 400010, Peoples R China; [Zhou, Yalan] Army Logist Acad, Chongqing 401311, Peoples R China; [Lin, Zhibin] Univ Durham, Business Sch, Durham, England; [Wang, Yu] Chongqing Univ, Sch Econ & Business Adm, Chongqing 400030, Peoples R China"

通信作者："Wang, Y (通讯作者)，Chongqing Univ, Sch Econ & Business Adm, Chongqing 400030, Peoples R China."

来源：ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS

ESI学科分类：ECONOMICS & BUSINESS

WOS号：WOS:001198928300001

JCR分区：Q1

影响因子：6

年份：2024

卷号：64

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Direct marketing; Response modeling; Imbalance classification; Dynamic ensemble learning

摘要："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 considering two factors. 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."

基金机构：China Postdoctoral Science Foundation [M2023M740440]; Chongqing Natural Science Foundation Postdoctoral Science Fund Project [CSTB2023NSCQ-BHX0107]; Chongqing Postdoctoral Program Special Foundation [2022CQBSHTB3065]

基金资助正文："This research is supported by the China Postdoctoral Science Foundation (M2023M740440) , the Chongqing Natural Science Foundation Postdoctoral Science Fund Project (CSTB2023NSCQ-BHX0107) and the Chongqing Postdoctoral Program Special Foundation (Grant No. 2022CQBSHTB3065) ."