A new filter feature selection approach for customer churn prediction in telecommunications

Yan-Jen Huang · B. Q. Huang · Tahar Kechadi

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

There is little literature to introduce the approaches for the feature selection, which plays an important role in the customer churn prediction. In addition, due to the imbalanced data classification problem occurring, most of the traditional approaches ineffectively select the important features for the churn prediction. This paper proposes a new filter feature selection approach for customer churn prediction in telecommunications. The main idea of this approach is to calculate the dependency between each input feature and the class. Finally, the comparative experiments were carried out, and the results show that the new proposed feature selection approach is very effective for the churn prediction.

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References

2005Applying data mining to telecom churn management 2002Turning telecommunications call details to churn prediction: a data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2003A novel evolutionary data mining algorithm with applications to churn prediction 2005Computer assisted customer churn management: State-of-the-art and future trends 2009Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications 2008Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques 2007Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service

Cited by

2013Churn prediction methodologies in the telecommunications sector: A survey 2021Design and Implementation of a System for Comparative Analysis of Learning Architectures for Churn Prediction 2014Improving churn prediction in telecommunications using complementary fusion of multilayer features based on factorization and construction 2016Research of indicator system in customer churn prediction for telecom industry

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