Title :
Study on Customer Churn Prediction Methods Based on Multiple Classifiers Combination
Author :
Xiao, Yao ; He, Changzheng ; Xiao, Jin
Author_Institution :
Sch. of Bus. Adm., Sichuan Univ., Chengdu, China
Abstract :
Combining multiple classifiers combination, sampling techniques, and more appropriate evaluation metrics, we first compare the selection of multiple classifiers combination based on GMDH(S-GMDH) and other classification methods on nine class imbalance data sets; we analyze the change of classification performances with and without using sampling. Then we further do customer churn prediction on `churn´ from the nine data sets. It is concluded that class imbalance has severely affected classification performances of various classifiers, which will surely influence churn prediction. Experiments prove that it is an effective way to improve churn prediction by combining S-GMDH and sampling techniques.
Keywords :
customer services; pattern classification; sampling methods; S-GMDH; class imbalance data sets; classification methods; customer churn prediction methods; evaluation metrics; multiple classifiers combination; sampling techniques; Data analysis; Helium; Information technology; Mathematical model; Performance analysis; Performance evaluation; Prediction methods; Sampling methods; Testing; Voting; GMDH; class imbalance; customer churn prediction; multiple classifiers combination; sampling;
Conference_Titel :
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3859-4
DOI :
10.1109/IITA.2009.190