Hybrid ensemble learning approaches to customer churn prediction
ISSN: 0368-492X
Article publication date: 27 May 2021
Issue publication date: 22 February 2022
Abstract
Purpose
Customer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.
Design/methodology/approach
In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.
Findings
To examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.
Originality/value
In this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.
Keywords
Acknowledgements
Human and animal rights statement: This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of interest: The authors of the paper do have any conflict of interest with any companies or institutions.
Citation
Tavassoli, S. and Koosha, H. (2022), "Hybrid ensemble learning approaches to customer churn prediction", Kybernetes, Vol. 51 No. 3, pp. 1062-1088. https://doi.org/10.1108/K-04-2020-0214
Publisher
:Emerald Publishing Limited
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