Learning trends in customer churn with rule-based and kernel methods

Nahier Aldhafferi, Abdullah Alqahtani, Fatema Sabeen Shaikh, Sunday Olusanya Olatunji, Abdullah Almurayh, Fahad A. Alghamdi, Ghalib H. Alshammri, Amani K. Samha, Mutasem Khalil Alsmadi, Hayat Alfagham, Abderrazak Ben Salah


In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature of predictions and rules obtained from them are able to provide a choice between a more focused or more extensive program the company may wish to implement as part of its customer retention program.


churn model; data science; decision tree; intelligent system; rough set theory; rule-based method; support vector machine;

Full Text:


DOI: http://doi.org/10.11591/ijece.v12i5.pp5364-5374

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).