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

2010 ◽  
Author(s):  
Y. Huang ◽  
B. Q. Huang ◽  
M. T. Kechadi
Keyword(s):  
Feature Selection ◽  
Churn Prediction ◽  
Customer Churn ◽  
Selection Approach ◽  
Customer Churn Prediction ◽  
Feature Selection Approach

Feature-selection-based dynamic transfer ensemble model for customer churn prediction

2014 ◽  
Vol 43 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Jin Xiao ◽  
Yi Xiao ◽  
Anqiang Huang ◽  
Dunhu Liu ◽  
Shouyang Wang
Keyword(s):  
Feature Selection ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning

2021 ◽  
Vol 5 (2) ◽  
pp. 62-72
Author(s):  
Asif Yaseen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Service Providers ◽  
Mobile Commerce ◽  
Personal Digital Assistants ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Feature Selection Technique ◽  
Customer Churn ◽  
Feature Selection Approach

With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.

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