Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector

Computing ◽  
2018 ◽  
Vol 100 (8) ◽  
pp. 839-860 ◽  
Author(s):  
J. Vijaya ◽  
E. Sivasankar
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Ensemble Classification ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Classification Techniques ◽  
Customer Churn ◽  
Customer Churn Prediction

Customer churn prediction in the telecommunication sector using a rough set approach

2017 ◽  
Vol 237 ◽  
pp. 242-254 ◽  
Author(s):  
Adnan Amin ◽  
Sajid Anwar ◽  
Awais Adnan ◽  
Muhammad Nawaz ◽  
Khalid Alawfi ◽  
...  
Keyword(s):  
Rough Set ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Customer Churn ◽  
Customer Churn Prediction

Just-in-time customer churn prediction in the telecommunication sector

2017 ◽  
Vol 76 (6) ◽  
pp. 3924-3948 ◽  
Author(s):  
Adnan Amin ◽  
Feras Al-Obeidat ◽  
Babar Shah ◽  
May Al Tae ◽  
Changez Khan ◽  
...  
Keyword(s):  
Just In Time ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals Customer Churn Prediction for Imbalanced Class Distribution of Data in Business Sector

2019 ◽  
Vol 5 ◽  
pp. 101-110
Author(s):  
Aayush Bhattarai ◽  
Elisha Shrestha ◽  
Ram Prasad Sapkota
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Telecommunications Industry ◽  
Business Case ◽  
Machine Learning Algorithms ◽  
Business Sector ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Customer Churn ◽  
Customer Churn Prediction

Churners are those people who are about to transfer their business to a competitor or simply who cancel a subscription to a service. This paper is based on a specific business sector, which is telecommunication sector. With a churn rate of 30%, the telecommunication sector takes the first place on the list. In this paper, we present some advanced data mining methodologies which predicts customer churn in the pre-paid mobile telecommunications industry using a call detail records dataset. To implement the predictive models, we initially propose and then apply four machine learning algorithms: Random Forest, Naïve Bayes, Logistic Regression, and XG Boost. To evaluate the models, we use various evaluation metrics and find the best model which will be suitable for any class imbalanced data and also our business case. This paper can also be viewed as a comparative study on the most popular machine learning methods applied to the challenging problem of customer churn prediction.

Combined rough set theory and flow network graph to predict customer churn in credit card accounts

2011 ◽  
Vol 38 (1) ◽  
pp. 8-15 ◽  
Author(s):  
Chiun-Sin Lin ◽  
Gwo-Hshiung Tzeng ◽  
Yang-Chieh Chin
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Credit Card ◽  
Rough Set Theory ◽  
Network Graph ◽  
Flow Network ◽  
Customer Churn

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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