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Article: ADTreesLogit model for customer churn prediction

TitleADTreesLogit model for customer churn prediction
Authors
Qi, JZhang, LLiu, YLi, LZhou, YShen, YLiang, LLi, H
KeywordsADTrees
Customer churn
Data mining
Logistic regression
Issue Date2009
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0254-5330
Citation
Annals Of Operations Research, 2009, v. 168 n. 1, p. 247-265 How to Cite?
DOI: http://dx.doi.org/10.1007/s10479-008-0400-8
AbstractIn this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet® on two important observation points. © 2008 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/157733
ISSN
0254-5330
2023 Impact Factor: 4.4
2023 SCImago Journal Rankings: 1.019
ISI Accession Number ID
WOS:000264317000014
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorQi, Jen_HK
dc.contributor.authorZhang, Len_HK
dc.contributor.authorLiu, Yen_HK
dc.contributor.authorLi, Len_HK
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorShen, Yen_HK
dc.contributor.authorLiang, Len_HK
dc.contributor.authorLi, Hen_HK
dc.date.accessioned2012-08-08T08:55:14Z-
dc.date.available2012-08-08T08:55:14Z-
dc.date.issued2009en_HK
dc.identifier.citationAnnals Of Operations Research, 2009, v. 168 n. 1, p. 247-265en_HK
dc.identifier.issn0254-5330en_HK
dc.identifier.urihttp://hdl.handle.net/10722/157733-
dc.description.abstractIn this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet® on two important observation points. © 2008 Springer Science+Business Media, LLC.en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0254-5330en_HK
dc.relation.ispartofAnnals of Operations Researchen_HK
dc.subjectADTreesen_HK
dc.subjectCustomer churnen_HK
dc.subjectData miningen_HK
dc.subjectLogistic regressionen_HK
dc.titleADTreesLogit model for customer churn predictionen_HK
dc.typeArticleen_HK
dc.identifier.emailZhou, Y: yongpin@hku.hken_HK
dc.identifier.authorityZhou, Y=rp01614en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s10479-008-0400-8en_HK
dc.identifier.scopuseid_2-s2.0-62949153791en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-62949153791&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume168en_HK
dc.identifier.issue1en_HK
dc.identifier.spage247en_HK
dc.identifier.epage265en_HK
dc.identifier.isiWOS:000264317000014-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridQi, J=13408299900en_HK
dc.identifier.scopusauthoridZhang, L=15039813600en_HK
dc.identifier.scopusauthoridLiu, Y=36014081600en_HK
dc.identifier.scopusauthoridLi, L=7501446452en_HK
dc.identifier.scopusauthoridZhou, Y=9037956000en_HK
dc.identifier.scopusauthoridShen, Y=37086829900en_HK
dc.identifier.scopusauthoridLiang, L=25632675700en_HK
dc.identifier.scopusauthoridLi, H=8575711600en_HK
dc.identifier.issnl0254-5330-

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