A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees

Arno de Caigny; Kristof Coussement; Koen W. de Bock

doi:10.1016/j.ejor.2018.02.009

Journal Articles European Journal of Operational Research Year : 2018

A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees

(1) , (1) , (2)

1
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Arno de Caigny

Function : Author

School of Management (LEM)

Kristof Coussement

Function : Author
PersonId : 1235018
ORCID : 0000-0003-1346-9425

School of Management (LEM)

Koen W. de Bock

Function : Author
PersonId : 737856
IdHAL : koen-w-de-bock
ORCID : 0000-0002-4872-9007
IdRef : 182936333

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Business administration Machine Learning [stat.ML] Methods and statistics

Koen W. De Bock : Connect in order to contact the contributor

https://hal-audencia.archives-ouvertes.fr/hal-01741661

Submitted on : Friday, March 23, 2018-12:57:09 PM

Last modification on : Tuesday, August 30, 2022-5:14:20 PM

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hal-01741661 , version 1 (23-03-2018)

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HAL Id : hal-01741661 , version 1
DOI : 10.1016/j.ejor.2018.02.009

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Arno de Caigny, Kristof Coussement, Koen W. de Bock. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 2018, ⟨10.1016/j.ejor.2018.02.009⟩. ⟨hal-01741661⟩

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A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees

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