Improving Resampling-based Ensemble in Churn Prediction

Bing Zhu, Seppe Broucke, Bart Baesens, Sebastián Maldonado
Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 74:79-91, 2017.

Abstract

Dealing with class imbalance is a challenging issue in churn prediction. Although resampling-based ensemble solutions have demonstrated their superiority in many fields, previous research shows that they cannot improve the profit-based measure in churn prediction. In this paper, we explore the impact of the class ratio in the training subsets on the predictive performance of resampling-based ensemble techniques based on experiments on real-world churn prediction data sets. The experimental results show that the setting of the class ratio has a great impact on the model performance. It is also found that by choosing suitable class ratios in the training subsets, UnderBagging and Balanced Random Forests can significantly improve profits brought by the churn prediction model. The demonstrated results provide some guidelines for both academic and industrial practitioners.

Cite this Paper

BibTeX
@InProceedings{pmlr-v74-zhu17a, title = {Improving Resampling-based Ensemble in Churn Prediction}, author = {Zhu, Bing and Broucke, Seppe and Baesens, Bart and Maldonado, Sebastián}, booktitle = {Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications}, pages = {79--91}, year = {2017}, editor = {Luís Torgo, Paula Branco and Moniz, Nuno}, volume = {74}, series = {Proceedings of Machine Learning Research}, month = {22 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v74/zhu17a/zhu17a.pdf}, url = {https://proceedings.mlr.press/v74/zhu17a.html}, abstract = {Dealing with class imbalance is a challenging issue in churn prediction. Although resampling-based ensemble solutions have demonstrated their superiority in many fields, previous research shows that they cannot improve the profit-based measure in churn prediction. In this paper, we explore the impact of the class ratio in the training subsets on the predictive performance of resampling-based ensemble techniques based on experiments on real-world churn prediction data sets. The experimental results show that the setting of the class ratio has a great impact on the model performance. It is also found that by choosing suitable class ratios in the training subsets, UnderBagging and Balanced Random Forests can significantly improve profits brought by the churn prediction model. The demonstrated results provide some guidelines for both academic and industrial practitioners.} }
Endnote
%0 Conference Paper %T Improving Resampling-based Ensemble in Churn Prediction %A Bing Zhu %A Seppe Broucke %A Bart Baesens %A Sebastián Maldonado %B Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications %C Proceedings of Machine Learning Research %D 2017 %E Paula Branco Luís Torgo %E Nuno Moniz %F pmlr-v74-zhu17a %I PMLR %P 79--91 %U https://proceedings.mlr.press/v74/zhu17a.html %V 74 %X Dealing with class imbalance is a challenging issue in churn prediction. Although resampling-based ensemble solutions have demonstrated their superiority in many fields, previous research shows that they cannot improve the profit-based measure in churn prediction. In this paper, we explore the impact of the class ratio in the training subsets on the predictive performance of resampling-based ensemble techniques based on experiments on real-world churn prediction data sets. The experimental results show that the setting of the class ratio has a great impact on the model performance. It is also found that by choosing suitable class ratios in the training subsets, UnderBagging and Balanced Random Forests can significantly improve profits brought by the churn prediction model. The demonstrated results provide some guidelines for both academic and industrial practitioners.
APA
Zhu, B., Broucke, S., Baesens, B. & Maldonado, S.. (2017). Improving Resampling-based Ensemble in Churn Prediction. Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 74:79-91 Available from https://proceedings.mlr.press/v74/zhu17a.html.

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