Ping Jiang , Zhenkun Liu , Mohammad Zoynul Abedin , Jianzhou Wang , Wendong Yang , Qingli Dong
{"title":"利润驱动的加权分类器具有可解释的客户流失预测能力","authors":"Ping Jiang , Zhenkun Liu , Mohammad Zoynul Abedin , Jianzhou Wang , Wendong Yang , Qingli Dong","doi":"10.1016/j.omega.2024.103034","DOIUrl":null,"url":null,"abstract":"<div><p>Customer churn prediction methods aim to identify customers with the highest probability of attrition, improve the effectiveness of customer retention campaigns, and maximize profits. However, previous studies have relied on a single classifier, leading to suboptimal predictive results. To address this issue, we propose a novel profit-driven weighted classifier that integrates a weighted strategy with multiple profit-driven ensemble members. We employ an artificial hummingbird optimization algorithm to determine the optimal weight coefficients of the profit-driven ensemble members based on the expected maximum profit criterion. We then calculate the Shapley additive explanation value to further improve the interpretability of the proposed weighted classifier. We conducted experiments and statistical tests on eight real-world datasets from different industries. The results show that the proposed weighted classifier significantly improves profits compared with comparative classifiers and provides strong interpretability based on the Shapley additive explanation value.</p></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Profit-driven weighted classifier with interpretable ability for customer churn prediction\",\"authors\":\"Ping Jiang , Zhenkun Liu , Mohammad Zoynul Abedin , Jianzhou Wang , Wendong Yang , Qingli Dong\",\"doi\":\"10.1016/j.omega.2024.103034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Customer churn prediction methods aim to identify customers with the highest probability of attrition, improve the effectiveness of customer retention campaigns, and maximize profits. However, previous studies have relied on a single classifier, leading to suboptimal predictive results. To address this issue, we propose a novel profit-driven weighted classifier that integrates a weighted strategy with multiple profit-driven ensemble members. We employ an artificial hummingbird optimization algorithm to determine the optimal weight coefficients of the profit-driven ensemble members based on the expected maximum profit criterion. We then calculate the Shapley additive explanation value to further improve the interpretability of the proposed weighted classifier. We conducted experiments and statistical tests on eight real-world datasets from different industries. The results show that the proposed weighted classifier significantly improves profits compared with comparative classifiers and provides strong interpretability based on the Shapley additive explanation value.</p></div>\",\"PeriodicalId\":19529,\"journal\":{\"name\":\"Omega-international Journal of Management Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Omega-international Journal of Management Science\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S030504832400001X\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S030504832400001X","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
Profit-driven weighted classifier with interpretable ability for customer churn prediction
Customer churn prediction methods aim to identify customers with the highest probability of attrition, improve the effectiveness of customer retention campaigns, and maximize profits. However, previous studies have relied on a single classifier, leading to suboptimal predictive results. To address this issue, we propose a novel profit-driven weighted classifier that integrates a weighted strategy with multiple profit-driven ensemble members. We employ an artificial hummingbird optimization algorithm to determine the optimal weight coefficients of the profit-driven ensemble members based on the expected maximum profit criterion. We then calculate the Shapley additive explanation value to further improve the interpretability of the proposed weighted classifier. We conducted experiments and statistical tests on eight real-world datasets from different industries. The results show that the proposed weighted classifier significantly improves profits compared with comparative classifiers and provides strong interpretability based on the Shapley additive explanation value.
期刊介绍:
Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.