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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Improving Customer Churn Classification with Ensemble Stacking Method

Author 1: Mohd Khalid Awang
Author 2: Mokhairi Makhtar
Author 3: Norlina Udin
Author 4: Nur Farraliza Mansor

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121132

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 11, 2021.

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Abstract: Due to the high cost of acquiring new customers, accurate customer churn classification is critical in any company. The telecommunications industry has employed single classifiers to classify customer churn; however, the classification accuracy remains low. Nevertheless, combining several classifiers' decisions improves classification accuracy. This article attempts to enhance ensemble integration via stack generalisation. This paper proposed a stacking ensemble based on six different learning algorithms as the base-classifiers and tested on five different meta-model classifiers. We compared the performance of the proposed stacking ensemble model with single classifiers, bagging and boosting ensemble. The performances of the models were evaluated with accuracy, precision, recall and ROC criteria. The findings of the experiments demonstrated that the proposed stacking ensemble model resulted in the improvement of the customer churn classification. Based on the results of the experiments, it indicates that the prediction accuracy, precision, recall and ROC of the proposed stacking ensemble with MLP meta-model outperformed other single classifiers and ensemble methods for the customer churn dataset.

Keywords: Stacking ensemble; customer churn prediction; bagging; boosting

Mohd Khalid Awang, Mokhairi Makhtar, Norlina Udin and Nur Farraliza Mansor, “Improving Customer Churn Classification with Ensemble Stacking Method” International Journal of Advanced Computer Science and Applications(IJACSA), 12(11), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121132

@article{Awang2021,
title = {Improving Customer Churn Classification with Ensemble Stacking Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121132},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121132},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {11},
author = {Mohd Khalid Awang and Mokhairi Makhtar and Norlina Udin and Nur Farraliza Mansor}
}


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