Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications

2010 ◽  
Vol 37 (5) ◽  
pp. 3638-3646 ◽  
Author(s):  
Bingquan Huang ◽  
B. Buckley ◽  
T.-M. Kechadi
Keyword(s):  
Feature Selection ◽  
Churn Prediction ◽  
Nsga Ii ◽  
Multi Objective ◽  
Customer Churn ◽  
Customer Churn Prediction

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

A Multi-objective Meta-Analytic Method for Customer Churn Prediction

2019 ◽  
pp. 781-813
Author(s):  
Mohammad Nazmul Haque ◽  
Natalie Jane de Vries ◽  
Pablo Moscato
Keyword(s):  
Analytic Method ◽  
Churn Prediction ◽  
Multi Objective ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Phong Thanh Nguyen ◽  
Mohamed Elhoseny ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Optimization Algorithm ◽  
Service Providers ◽  
Sampling Rate ◽  
Optimal Sampling ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Multi Objective ◽  
Customer Churn ◽  
Customer Churn Prediction

AbstractCustomer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

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