Improving Customer Churn Classification with Ensemble Stacking Method

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.

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