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It introduces IADASYN-FLCatBoost from a dual perspective. At the data level, ADASYN is enhanced by using LOF to remove outliers and synthesizing minority samples, forming IADASYN. At the algorithm level, Focal Loss is embedded into CatBoost to create cost-sensitive FLCatBoost. Experiments on a Kaggle credit-card dataset show superior recall, F1, G-mean, and AUPRC versus baselines and additional sampling/ensemble classifiers, with demonstrated generalizability across banks and industries.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/imbalanced-customer-churn-classification-using-a-new-multi-strategy-collaborative-processing-method/606621/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/imbalanced-customer-churn-classification-using-a-new-multi-strategy-collaborative-processing-method/606621.png","ImageObject",300,407,{"name":92,"@type":93},"eBook King","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-10-16",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address in customer churn prediction?","Question",{"text":112,"@type":113},"It addresses the challenge of accurately classifying imbalanced customer datasets for churn prediction in banking, where typical models struggle with minority-class representation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is IADASYN-FLCatBoost designed at the data level?",{"text":117,"@type":113},"It improves ADASYN by introducing LOF to eliminate outliers and by specially synthesizing new minority-class samples, resulting in the IADASYN method.",{"name":119,"@type":110,"acceptedAnswer":120},"What improvement is made at the algorithm level compared with standard CatBoost?",{"text":121,"@type":113},"Focal Loss is embedded into the CatBoost ensemble framework to create FLCatBoost, making the model focal-aware and cost-sensitive for imbalanced churn classification.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},606621,1792440534,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962088006270,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Expert Systems With Applications 247 (2024) 123251  \nContents lists available at ScienceDirect  \nExpert Systems With Applications  \njournal [homepage:](homepage: www.elsevier.com/locate/eswa)[ www.elsevier.com/locate/eswa](homepage: www.elsevier.com/locate/eswa)  \n| Imbalanced customer churn classification using a new multi-strategy collaborative processing method |  |  | |\n| --- | --- | --- | --- |\n| Congjun Rao a, Yaling Xu a, *, Xinping Xiao a, Fuyan Hua, Mark Goh b\u003Cbr>a School of Science, Wuhan University of Technology, Wuhan 430070, PR China\u003Cbr>b NUS Business School & The Logistics Institute-Asia Pacific, National University of Singapore, 119623, Singapore |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Imbalanced customer data\u003Cbr>Multi-strategy collaborative processing IADASYN\u003Cbr>FLCatBoost |  | The rapid advancement of big data and artificial intelligence heralds a dual-edged era of opportunities and challenges for the banking sector. Indeed, enhancing a model’s capability to accurately classify imbalanced datasets represents a critical challenge within the field of customer churn prediction (CCP). In this paper, to address the challenges presented by the problem of imbalanced customer classification, a new multi-strategy collaborative processing method named IADASYN-FLCatBoost is proposed from dual perspectives: data and algorithm. At the data level, the traditional Adaptive Synthetic (ADASYN) sampling is improved, that is, the LOF (Local Outlier Factor) algorithm is introduced to eliminate outliers, and the classification features are specially processed to synthesize new minority class samples, thus an improved ADASYN (IADASYN) algorithm is obtained. At the algorithm level, the Focal Loss is embedded into the CatBoost ensemble learning framework to form a new Focal Loss-CatBoost (FLCatBoost) to make a focal-aware, cost-sensitive version of imbalanced customer churn prediction. Moreover, the empirical analysis is conducted in conjunction with the credit card customer dataset obtained from the Kaggle platform. The results of the staged comparison experiments show that the proposed method IADASYN-FLCatBoost in this paper shows the best prediction performance. Comparing the proposed method with 5 other imbalanced classification algorithms and 20 classifiers composed of classical sampling methods and ensemble learning algorithms, it is verified that the classification effect of the proposed method performs best, and the values of Recall, F1 score, G-mean and Area under Precision-Recall curve (AUPRC) have been significantly improved. In addition, further verification of the model also proves that the proposed method has certain generalizability and is still valid for other banks and customer churn datasets of other industries. |  |\n\n1. Introduction  \nWith the rapid development of emerging technologies such as artificial intelligence, big data and cloud computing, the banking industry, as one of the most important industries in modern society, has also ushered in the Bank 4.0 era of invisibility and intelligence (Wang, & Ji, 2018). Along with it, there is a fierce competition among major banks. Credit card business, as the main business module of banks, still attracts many consumers due to its high reliability, low interest rate and high credit limit service advantages in the context of smart finance. However, with the increase in the number of cards issued by major banks and the excessive homogenization of products and services, credit card customer churn has emerged as a significant and complex challenge. It is found  \nthat reducing the customer churn rate can improve the profitability of banks to some extent. As a result, more and more banking professionals are shifting from a “product-centric” marketing philosophy to a“customer-centric” one in an era of competitor emergence and business diversification (Larivi`ere, & Van den Poel, 2004; Wu, Li, Zhao, & Liu, 2022). The banks are a data-oriente","cbCaitB23Ynr8ReT","https://ap.wps.com/l/cbCaitB23Ynr8ReT","pdf",3870700,"English","# Introduction\n## Background and problem motivation\n# Related methods and learning frameworks\n# Proposed method: IADASYN-FLCatBoost\n## Data-level improvements\n## Algorithm-level improvements\n# Experimental evaluation\n## Dataset and experimental setup\n## Performance comparison\n# Conclusion and generalizability","[{\"question\":\"What problem does the paper address in customer churn prediction?\",\"answer\":\"It addresses the challenge of accurately classifying imbalanced customer datasets for churn prediction in banking, where typical models struggle with minority-class representation.\"},{\"question\":\"How is IADASYN-FLCatBoost designed at the data level?\",\"answer\":\"It improves ADASYN by introducing LOF to eliminate outliers and by specially synthesizing new minority-class samples, resulting in the IADASYN method.\"},{\"question\":\"What improvement is made at the algorithm level compared with standard CatBoost?\",\"answer\":\"Focal Loss is embedded into the CatBoost ensemble framework to create FLCatBoost, making the model focal-aware and cost-sensitive for imbalanced churn classification.\"}]","Imbalanced customer churn classification using a new multi-strategy collaborative processing method | PDF",1792185308,48]