Model of Customer Churn Prediction on Support Vector Machine

To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural networ...

Full description

Saved in:
Bibliographic Details
Published inSystems engineering (Amsterdam) Vol. 28; no. 1; pp. 71 - 77
Main Authors XIA, Guo-en, JIN, Wei-dong
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2008
Subjects
customer churn
support vector machine (SVM)
telecommunication industry
telecommunication industry
customer churn
support vector machine (SVM)
Online AccessGet full text

Cover

More Information
Summary:To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural network, decision tree, logistic regression, and naive bayesian classifier. It is found that the method enjoys the best accuracy rate, hit rate, covering rate, and lift coefficient, and therefore, provides an effective measurement for customer churn prediction.
ISSN:1874-8651
1874-8651
DOI:10.1016/S1874-8651(09)60003-X