Handling class imbalance in customer churn prediction

By Jonathan Burez, Dirk Van den Poel. Expert systems with applications, 2009

570 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, background): “Burez and Van den Poel (2009) have studied customer churn prediction against class imbalance problem using sampling and modeling techniques such as weighted RF and gradient boosting.”
New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, methodology): “…adopted for churn prediction, including traditional statistical methods such as logistic regression (Lemmens and Croux, 2006; Neslin et al., 2006; Burez and Van den Poel, 2009), non-parametric statistical models like for instance k-nearest neighbor (Datta et al., 2000), decision trees (Wei and…”
Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases (2016, background): “several reasons behind this behavior [10,11], some causes are:”
Customer churn prediction in telecom using machine learning in big data platform (2019, methodology): “This method is the same as the one used in more than one research papers [8, 26].”
Churn in Social Networks (2010, methodology): “Further, [8] concludes that advanced sampling techniques do not provide significant improvement and that advanced classification techniques, such as cost-sensitive learners [67] like weighted random forests [10] and boosting [19], should always be compared to logistic regression.”