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Reseach Article

A Neuro-Fuzzy Classifier for Customer Churn Prediction

by Hossein Abbasimehr, Mostafa Setak, M. J. Tarokh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 8
Year of Publication: 2011
Authors: Hossein Abbasimehr, Mostafa Setak, M. J. Tarokh

Hossein Abbasimehr, Mostafa Setak, M. J. Tarokh . A Neuro-Fuzzy Classifier for Customer Churn Prediction. International Journal of Computer Applications. 19, 8 ( April 2011), 35-41. DOI=10.5120/2379-3138

@article{ 10.5120/2379-3138,
author = { Hossein Abbasimehr, Mostafa Setak, M. J. Tarokh },
title = { A Neuro-Fuzzy Classifier for Customer Churn Prediction },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 8 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { },
doi = { 10.5120/2379-3138 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:06:34.911618+05:30
%A Hossein Abbasimehr
%A Mostafa Setak
%A M. J. Tarokh
%T A Neuro-Fuzzy Classifier for Customer Churn Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 8
%P 35-41
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensible. Therefore, Adaptive Neuro Fuzzy Inference System (ANFIS) as neuro-fuzzy classifier is applied to churn prediction modeling and benchmarked to traditional rule-based classifier such as C4.5 and RIPPER. In this paper, we have built two ANFIS models including ANFIS-Subtractive (subtractive clustering based fuzzy inference system (FIS)) and ANFIS-FCM (fuzzy C-means (FCM) based FIS) models. The results showed that both ANFIS-Subtractive and ANFIS-FCM models have acceptable performance in terms of accuracy, specificity, and sensitivity. In addition, ANFIS-Subtractive and ANFIS-FCM clearly induce much less rules than C4.5 and RIPPER. Hence ANFIS-Subtractive and ANFIS-FCM are the most comprehensible techniques tested in the experiments. These results indicate that ANFIS shows acceptable performance in terms of accuracy and comprehensibility, and it is an appropriate choice for churn prediction applications.

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Index Terms

Computer Science
Information Sciences


Churn Prediction Data mining ANFIS Fuzzy C-means Subtractive clustering