Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine

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dc.contributor.authorZhao, Xiko
dc.contributor.authorShi, Yongko
dc.contributor.authorLee, Jongwonko
dc.contributor.authorKim, Heung Keeko
dc.contributor.authorLee, Heeseokko
dc.date.accessioned2014-12-16-
dc.date.available2014-12-16-
dc.date.created2014-08-20-
dc.date.created2014-08-20-
dc.date.issued2014-09-
dc.identifier.citationINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, v.13, no.5, pp.1013 - 1027-
dc.identifier.issn0219-6220-
dc.identifier.urihttp://hdl.handle.net/10203/192680-
dc.description.abstractBank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classiffication technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications.-
dc.languageEnglish-
dc.publisherWORLD SCIENTIFIC PUBL CO PTE LTD-
dc.titleCustomer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine-
dc.typeArticle-
dc.identifier.wosid000341843900007-
dc.identifier.scopusid2-s2.0-84929320327-
dc.type.rimsART-
dc.citation.volume13-
dc.citation.issue5-
dc.citation.beginningpage1013-
dc.citation.endingpage1027-
dc.citation.publicationnameINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING-
dc.identifier.doi10.1142/S0219622014500680-
dc.contributor.localauthorLee, Heeseok-
dc.contributor.nonIdAuthorZhao, Xi-
dc.contributor.nonIdAuthorShi, Yong-
dc.contributor.nonIdAuthorLee, Jongwon-
dc.contributor.nonIdAuthorKim, Heung Kee-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCustomer churn-
dc.subject.keywordAuthormaximal information coefficient-
dc.subject.keywordAuthornonparallel support vector machine-
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